Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Multi-Byzantine Fault Tolerant (Multi-BFT) consensus, which runs multiple BFT instances in parallel, has recently emerged as a promising approach to overcome the leader bottleneck in classical BFT protocols. However, existing designs rely on a global ordering layer to serialize blocks across instances, an intuitive yet costly mechanism that constrains scalability, amplifies failure propagation, and complicates deployment. In this paper, we challenge this conventional wisdom. We present HYDRA, the first Multi-BFT consensus framework that eliminates global ordering altogether. HYDRA introduces an object-centric execution model that partitions transactions by their accessed objects, enabling concurrent yet deterministic execution across instances. To ensure consistency, HYDRA combines lightweight lock-based coordination with a deadlock resolution mechanism, achieving both scalability and correctness. We implement HYDRA and evaluate it on up to 128 replicas in both LAN and WAN environments. Experimental results show HYDRA outperforms several state-of-the-art Multi-BFT protocols in the presence of a straggler. These results demonstrate strong consistency and high performance by removing global ordering, opening a new direction toward scalable Multi-BFT consensus design.

In end-to-end encrypted (E2EE) messaging systems, protecting communication metadata, such as who is communicating with whom, at what time, etc., remains a challenging problem. Existing designs mostly fall into the balancing act among security, performance, and trust assumptions: 1) designs with cryptographic security often use hefty operations, incurring performance roadblocks and expensive operational costs for large-scale deployment; 2) more performant systems often follow a weaker security guarantee, like differential privacy, and generally demand more trust from the involved servers. So far, there has been no dominant solution. In this paper, we take a different technical route from prior art, and propose Boomerang, an alternative metadata-private messaging system leveraging the readily available trust assumption on secure enclaves (as those emerging in the cloud). Through a number of carefully tailored oblivious techniques on message shuffling, workload distribution, and proactive patching of the communication pattern, Boomerang brings together low latency, horizontal scalability, and cryptographic security, without prohibitive extra cost. With 32 machines, Boomerang achieves 99th percentile latency of 7.76 seconds for$2^{20}$clients. Upon Boomerang, we also propose and implement a new client instantiation based on modern web browser extensions. We hope Boomerang offers attractive alternative options to the current landscape of metadata-private messaging designs.

For those seeking end-to-end private communication free from pervasive metadata tracking and censorship, the Tor network has been the de-facto choice in practice, despite its susceptibility to traffic analysis attacks. Recently, numerous metadata-private messaging proposals have emerged with the aim to surpass Tor in the messaging context by obscuring the relationships between any two messaging buddies, even against global and active attackers. However, most of these systems face an undesirable usability constraint: they require a metadata-private “dialing” phase to establish mutual agreement and timing or round coordination before initiating any regular chats among users. This phase is not only resource-intensive but also inflexible, limiting users’ ability to manage multiple concurrent conversations seamlessly. For stringent privacy requirement, the often-enforced traffic uniformity further exacerbated the limitations of this roadblock. In this paper, we introduce PingPong, a new end-to-end system for metadata-private messaging designed to overcome these limitations. Under the same traffic uniformity requirement, PingPong replaces the rigid “dial-before-converse” paradigm with a more flexible “notify-before-retrieval” workflow. This workflow incorporates a metadata-private notification subsystem, PING, and a metadata-private message store, PONG. Both PING and PONG leverage hardware-assisted secure enclaves for performance and operates through a series of customized oblivious algorithms, while meeting the uniformity requirements for metadata protection. By allowing users to switch between conversations on demand, PingPong achieves a level of usability akin to modern instant messaging systems, while also offering improved performance and bandwidth utilization for goodput We have built a prototype of PingPong with 32 8-core servers equipped with enclaves and conducted a case study on a real-world messaging metadata dataset to validate our claims.

As encrypted traffic grows, traditional rule-based and deep learning methods struggle with engineering costs and encryption complexity. While Large Language Models (LLMs) offer promise for traffic analysis via pre-trained feature learning, they face challenges in handling diverse tasks, retaining pre-training knowledge, and adapting efficiently. To address these issues, we propose a new traffic representation learning method and a new Parameter-Efficient Fine-Tuning (PEFT) method for multi-task encrypted traffic analysis services, calledTrafficLLM.TrafficLLMalleviates task heterogeneity by utilizing a universal multi-task prompt template and addresses pre-training knowledge forgetting by integrating Singular Value Decomposition based Low-Rank Adaptation (SVD-LoRA). To further reduce the cost of adapting to multiple tasks, we combine the strengths of the Mixture of Experts (MoE) for multi-task learning with SVD-LoRA for PEFT, enabling efficient multi-task traffic analysis. Additionally, we introduce task-aware gating functions to dynamically assign different weights to experts, facilitating the efficient fusion of expert knowledge. Comprehensive experiments on 7 datasets across 5 downstream tasks demonstrate thatTrafficLLMdelivers superior analysis performance and resource efficiency compared to state-of-the-art models, including DeepSeek, NetGPT, ET-BERT, and TFE-GNN. Detailed analysis of throughput, memory usage, and latency further highlights the practical advantages ofTrafficLLM. Our data and code are available athttps://github.com/yiliucs/TrafficLLMhttps://github.com/yiliucs/TrafficLLM.

Auditing Large Language Models (LLMs) is a crucial and challenging task. In this study, we focus on auditing black-box LLMs without access to their parameters, only to the provided service. We treat this type of auditing as a black-box optimization problem where the goal is to automatically uncover input-output pairs of the target LLMs that exhibit illegal, immoral, or unsafe behaviors. For instance, we may seek a non-toxic input that the target LLM responds to with a toxic output or an input that induces the hallucinative response from the target LLM containing politically sensitive individuals. This black-box optimization is challenging due to the scarcity of feasible points, the discrete nature of the prompt space, and the large search space. To address these challenges, we propose Curiosity-Driven Auditing for Large Language Models (CALM), which uses intrinsically motivated reinforcement learning to finetune an LLM as the auditor agent to uncover potential harmful and biased input-output pairs of the target LLM. CALM successfully identifies derogatory completions involving celebrities and uncovers inputs that elicit specific names under the black-box setting. This work offers a promising direction for auditing black-box LLMs.

To address the growing demand for privacy protection in machine learning, we propose an efficient and exact machine unlearning method for Large Models, called LMEraser. LMEraser takes a divide-and-conquer strategy with an adaptive prompt tuning mechanism to isolate data influence effectively. The training dataset is partitioned into public and private datasets. Public data are used to train the backbone of the model. Private data are clustered based on their diversity, and each cluster tunes a tailored prompt independently. This approach enables targeted unlearning by updating affected prompts, significantly reduces unlearning costs and maintains high model performance. Evaluations show that LMEraser reduces unlearning costs by 100 times compared to prior work without compromising model utility.

Data valuation quantifies the contribution of each training data, enabling harmful data detection and enhancing model robustness. However, existing methods are typically post-hoc and require fully trained models, making them computationally expensive and unable to detect harmful data early in training. We propose LiveVal, a real-time and trajectory-based data valuation method that assesses training data by analyzing their influence on the optimization trajectory. LiveVal includes three key innovations: 1) a real-time valuation framework with minimal overhead, seamlessly integrated into standard training processes; 2) an adaptive reference point mechanism that assesses data impact on generalization; and 3) a normalization technique that ensures fair comparisons across training stages. Theoretical analysis shows that LiveVal achieves directional alignment, boundedness, stability, and fairness. Experiments demonstrate that LiveVal achieves up to 180× speedup over baseline methods while maintaining robust performance across diverse models and datasets.

Developing reliable defenses against patch attacks on object detectors has attracted increasing interest. However, we identify that existing defense evaluations lack a unified and comprehensive framework, resulting in inconsistent and incomplete assessments of current methods. To address this issue, we revisit 11 representative defenses and present the first patch defense benchmark, involving 2 attack goals, 13 patch attacks, 11 object detectors, and 4 diverse metrics. This leads to the large-scale adversarial patch dataset with 94 types of patches and 94,000 images. Our comprehensive analyses reveal new insights: (1) The difficulty in defending against naturalistic patches lies in the data distribution, rather than the commonly believed high frequencies. Our new dataset with diverse patch distributions can be used to improve existing defenses by 15.09% [email protected]. (2) The average precision of the attacked object, rather than the commonly pursued patch detection accuracy, shows high consistency with defense performance. (3) Adaptive attacks can substantially bypass existing defenses, and defenses with complex/stochastic models or universal patch properties are relatively robust. We hope that our analyses will serve as guidance on properly evaluating patch attacks/defenses and advancing their design. Code and dataset are available at https://github.com/Gandolfczjh/APDE, where we will keep integrating new attacks/defenses.

As the complexity of Blockchain-based Smart Contracts grows, users rely on remote pre-execution services to simulate the behavior of transactions before emitting them on-chain. However, users may be concerned that a dishonest service provider (SP) may leak their execution trace to frontrun them or respond with fake results to mislead them. Benign SPs tried to run their services in trusted execution environments (TEEs) to prove their honesty. However, existing service software and TEEs have attack surfaces for side channel or control flow attacks. Meanwhile, the problem of users’ intention leakage through world state access patterns remains unsolved.This paper proposes HarDTAPE, a hardware-dedicated trusted transaction pre-executor, to protect the confidentiality and integrity of pre-executed transactions against dishonest SPs. Here, "dedicated" has two meanings: the pre-execution service is implemented as dedicated hardware to guarantee a valid control flow, and each set of hardware is isolated and dedicated to at most one user within each session to eliminate side-channel attacks on shared hardware (e.g., cache evict-and-reload). For access pattern confidentiality, we use Path ORAM to store the world state reassembled into fixed-size pages. We also use pagewise code prefetching to prevent the query type from being recognized. We implemented HarDTAPE on a CPU + FPGA SoC as a proof-of-concept. Using transactions from real-world Ethereum Mainnet blocks as test cases, we show that HarDTAPE has an acceptable run time overhead and throughput.

Deep Neural Networks (DNNs) play a crucial role in the smart Internet of Things (IoT), with widespread applications in inference tasks like interactive games, intelligent driving, and augmented reality. Along with these promising applications, various task-offloading methods were proposed to improve the utilization of system resources, given that DNN model inference typically requires substantial computational power. However, existing offloading methods focus primarily on a specific model, and research addressing heterogeneous DNN models (with different structures and layers) remains limited in practical IoT environments. Directly integrating these methods would require frequent re-initialization to adapt to changes in the search space during the offloading of mixed heterogeneous DNN inference tasks, resulting in insufficient flexibility and the waste of computational resources. Thus, we propose AutoCut, a global heterogeneous model offloading service based on a customized multi-objective differential evolution algorithm, to find low-latency and energyefficient offloading partitions. AutoCut utilizes a group-layer granularity partitioning that avoids frequent changes in the search space when continuously offloading heterogeneous DNN inference tasks, thereby improving search efficiency. Experiments with six popular models show that AutoCut significantly improves inference performance regarding latency and energy efficiency.

The SCION Next-Generation Network (NGN) architecture has expanded steadily since 2017, with today 20+ ISPs offering SCION connectivity. In production, IP-to-SCION-to-IP translation by SCION-IP-Gateways (SIGs) is used, such that applications are unaware of the NGN communication. To accelerate innovation and deployments, our aim is to increase the number of native SCION use cases, where the application is fully SCION-aware and optimizes communication across all path choices offered by the network. We set out to achieve two core objectives: (1) facilitating simple native connectivity for applications, and (2) enhancing the scalability of SCION deployment at academic sites.

The Web of Things (WoT) enhances interoperability across webbased and ubiquitous computing platforms while complementing existing IoT standards.The multimodal Federated Learning (FL) paradigm has been introduced to enhance WoT by enabling the fusion of multi-source mobile sensing data while preserving privacy.However, a key challenge in mobile sensing systems using multimodal FL is modality incompleteness, where some modalities may be unavailable or only partially captured, potentially degrading the system's performance and reliability.Current multimodal FL frameworks typically train multiple unimodal FL subsystems or apply interpolation techniques on the node side to approximate missing modalities.However, these approaches overlook the shared latent feature space among incomplete modalities across different nodes and fail to discriminate against low-quality nodes.To address this gap, we present FedMobile, a new knowledge contribution-aware multimodal FL framework designed for robust learning despite missing modalities.FedMobile prioritizes local-to-global knowledge transfer, leveraging cross-node multimodal feature information to reconstruct missing features.It also enhances system performance and resilience to modality heterogeneity through rigorous node contribution assessments and knowledge contribution-aware aggregation rules.Empirical evaluations on five widely recognized multimodal benchmark datasets demonstrate that FedMobile maintains robust learning even when up to 90% of modality information is missing or when data from two modalities are randomly missing, outperforming state-of-the-art baselines.Our code and data are available at the link.

Multi-party computation (MPC) has gained increasing attention in both research and industry, with many protocols adopting the preprocessing model to optimize online performance through the strategic use of offline-generated, data-independent correlated randomness (or correlation). However, while extensive research has been dedicated to enhancing the online phase, the equally critical offline phase remains largely overlooked. This gap imposes significant yet unaddressed challenges in both security and efficiency, hindering the practical adoption of MPC systems. To address these challenges, we build upon the pseudorandom correlation generator (PCG) concept by Boyle et al. (CRYPTO’19, FOCS’20) and propose HPCG, a programmable, verifiable, and concretely efficient PCG construction using small security hardware. Our core technique, termed verifiable silent preprocessing, enables virtually unbounded, on-demand generation of diverse correlated randomness with provable correctness while effectively reducing offline overhead in a correlation-agnostic manner. To demonstrate the benefits of our approach, we experimentally evaluate HPCG and compare it with other preprocessing techniques. We also show how HPCG can further optimize specialized secure computation tasks (e.g., shuffling and equality test) by promoting new, customized correlations, which may be of new interest.

Federated Learning (FL) enables geographically distributed clients to collaboratively train machine learning models by exchanging local model parameters while preserving data privacy. In practice, FL faces two critical challenges. First, it is vulnerable to security issues as malicious clients would artificially harm the functionality of FL by launching poisoning attacks. Second, the inherent data heterogeneity among clients (termed Non-IID data in FL) naturally arises from distributed data ownership and significantly degrades model convergence and accuracy. However, with studies separately devoted to these two research lines, the interplay between data heterogeneity and security remains poorly understood. In this paper, we systematically investigate the relationship between data heterogeneity and adversarial robustness in FL. Specifically, we propose novel data partitioning algorithms that simulate Label-Conditional Non-IID and Feature-Conditional Non-IID with quantifiable heterogeneity levels. Further, we conduct extensive experiments to evaluate classical defense methods in the practical FL environment under state-of-the-art untargeted attacks. With results in various settings, we separately analyze the connection between Non-IID to defenses and attacks. Regarding attacks, with similar effects on models, Non-IID impacts the training in a different way compared with attacks. The interaction between attacks and Non-IID provides an opportunity to cause severe damage to FL. Regarding defenses, Non-IID induces heterogeneity in model distribution among clients which raises the difficulty of maintaining fidelity and robustness for defense methods.

Multi-port chargers, designed to simultaneously charge multiple mobile devices such as smartphones, have gained significant popularity, with millions of units sold in recent years. However, this multi-device charging feature introduces security and privacy risks. If not properly designed and implemented, these chargers can enable communication between connected devices because they are inherently interconnected, which leads to crosstalk voltage leakages. Despite their widespread use, these risks have not been thoroughly investigated. We have identified novel attack surfaces in the circuit design of multi-port chargers that allow an adversary who shares the multi-port charger with the target victim in close proximity to exploit one port to (i) recognize fine-grained user activities of other devices being charged, (ii) eavesdrop on secret audio transmission from USB-C audio pins, and (iii) inject malicious audio commands into built-in voice assistants of charging devices (e.g., Siri, Google Assistant). In this paper, we design and implement XPORTHEFT, a novel system to analyze and demonstrate the uncovered security and privacy threats in multi-port chargers. Specifically, it leverages changes in voltage signals in one neighbor port to monitor voltage changes in the charging port induced by user activities in various user interfaces, such as recognizing running apps and detecting keystrokes. Moreover, XPORTHEFT can also achieve audio transmission eavesdropping and launch inaudible audio injection attacks from the neighbor port to the charging mobile device via the USB-C interface. We extensively evaluate the effectiveness of XPORTHEFT using five commercial multi-port chargers and five mobile devices. The evaluation results show its high effectiveness in recognizing the launch of 20 mobile apps (88.7%) and revealing unlocking passcodes (98.8%), as well as eavesdropping on the audios of numeric digits (97.1%) and alphabetic characters (98.0%). Furthermore, XPORTHEFT achieves 100% success rates in inaudible audio injection attacks on three commercial voice assistants. In addition, our study also shows that XPORTHEFT is resilient to various impact factors and presents the potential to attack multiple victims.

SONICUMOS is an enhanced behavior-based face liveness detection system that combines ultrasonic and video signals to sense the 3D head gestures. As face authentication becomes increasingly prevalent, the need for a reliable liveness detection system is paramount. Traditional behavior-based liveness detection methods (e.g., eye-blinking, nodding, etc.), which are widely deployed in mission-critical scenarios like finance and banking applications today, are prone to advanced media-based facial forgery attacks. SONICUMOS aims to incorporate the traditional behaviorbased method for active liveness detection without introducing extra user burden. By employing ultrasonic signals, SONICUMOS capitalizes on the head gestures, significantly raising the security bar. Our approach utilizes the frequency-modulated continuouswave (FMCW) ultrasonic radar for robust 3D gesture recognition compatible with face authentication. We also propose a new dual-feature fusion network that integrates audio and video features at the feature level to increase detection accuracy and resilience against numerous attacks. Our prototype has been tested on seven off-the-shelf Android/iOS smartphones, achieving an overall detection accuracy of 95.83% at an equal error rate (EER) of 4.96% when dealing with 3D impersonation attacks

Encrypted range schemes are becoming increasingly attractive for commercial databases, as they allow for confidential query service on encrypted databases hosted on remote servers. These schemes, by design, leak specific patterns such as access, volume, and search patterns. However, they are vulnerable to leakage-abuse attacks (LAAs) that exploit these patterns to reconstruct the plaintext databases. In response, the query control paradigms have emerged, with our preceding framework,RangeQC, being a notable example. These paradigms probe deeper into the intricacies of granular user query access control, advancing beyond past scheme-level efforts and acting as sentinels against the inadvertent leakage of delicate data patterns. WhileRangeQCaimed to regulate high-leakage queries through query control, it encountered usability impediments. Acknowledging that query control alone might be insufficient, we introduce an additional layer of protection in our evolved framework,RangeQC+. This fusion model combines query control with differential privacy-based data perturbation, a proactive strategy to muddle query responses and yield obfuscated leakage patterns. Complementing this approach,RangeQC+incorporates refined, noise-resistant leakage metrics for accurate pattern analysis. Through comprehensive assessments and comparative analysis,RangeQC+consistently showcases a balanced blend of enhanced performance, robust privacy, and user-friendly functionality.

End-to-end encrypted messaging services (EEMSs) empower private communication through encrypting messages, yet also make content moderation for combating the spread of abusive messages challenging. There is an urgent call for supporting content moderation in EEMSs while ensuring user privacy. In this paper, we present a new system design for privacy-assured content moderation in EEMSs. At a high level, users in our system can privately report abusive messages, and the EEMS traces the source if a message has an aggregated report count exceeding a predefined threshold and is audited to be abusive. Our system mainly departs from prior works in that it allows flexible and adaptable thresholds, offers robustness against dishonest reporters providing malformed reports, and better ensures the privacy of all users during the moderation process. We also take a step further and propose a privacy-aware detection mechanism that relies on a blocklist built with transparency to mitigate the further spread of identified abusive messages from forwarders. Formal security analysis is provided and extensive experiments demonstrate the practical efficiency of our system.

Reinforcement learning agents are susceptible to evasion attacks during deployment. In single-agent environments, these attacks can occur through imperceptible perturbations injected into the inputs of the victim policy network. In multi-agent environments, an attacker can manipulate an adversarial opponent to influence the victim policy's observations indirectly. While adversarial policies offer a promising technique to craft such attacks, current methods are either sample-inefficient due to poor exploration strategies or require extra surrogate model training under the black-box assumption. To address these challenges, in this paper, we propose Intrinsically Motivated Adversarial Policy (IMAP) for efficient black-box adversarial policy learning in both single- and multi-agent environments. We formulate four types of adversarial intrinsic regularizers—maximizing the adversarial state coverage, policy coverage, risk, or divergence—to discover potential vulnerabilities of the victim policy in a principled way. We also present a novel bias-reduction method to balance the extrinsic objective and the adversarial intrinsic regularizers adaptively. Our experiments validate the effectiveness of the four types of adversarial intrinsic regularizers and the bias-reduction method in enhancing black-box adversarial policy learning across a variety of environments. Our IMAP successfully evades two types of defense methods, adversarial training and robust regularizer, decreasing the performance of the state-of-the-art robust WocaR-PPO agents by 34%-54% across four single-agent tasks. IMAP also achieves a state-of-the-art attacking success rate of 83.91% in the multi-agent game YouShallNotPass. Our code is available at https://github.com/x-zheng16/IMAP.

Tor bridges are crucial, unlisted relays designed to enhance system accessibility and circumvent censorship in the Tor network. Currently, Tor BridgeDB will randomly distribute 1–3 bridge relays to the user per request. Yet, those randomly selected bridges may not meet users' specific needs, e.g., adequate bandwidth for large-file sharing in a certain region. Also, a user's usage metadata (e.g., bridge choices) collected by Tor BridgeDB would inevitably reveal sensitive information about the user, discouraging the use of this censorship-circumvention service. In light of them, we introduce HiddenTor, a user-centric and privacy-focused bridge distribution system that allows Tor users to retrieve bridges privately and precisely (i.e., based on a range of specific criteria). At its core, HiddenTor designs a condition-based private information retrieval (PIR) protocol by building atop a suite of lightweight cryptographic primitives (i.e., function secret sharing). Besides, HiddenTor also crafts several optimization designs to balance the trade-offs between query efficiency and service reliability. The extensive experimental results have confirmed the feasibility and practicality of HiddenTor. For example, our prototype can efficiently handle private queries over 3000 bridges in approximately 2 seconds, which can further be reduced to 0.21 seconds using parallel computing techniques.

The quality of federated learning (FL) is highly correlated with the number and quality of the participants involved. It is essential to design proper contribution evaluation mechanisms. Shapley Value (SV)-based techniques have been widely used to provide fair contribution evaluation. Existing approaches, however, do not support dynamic participants (e.g., joining and departure) and incur significant computation costs, making them difficult to apply in practice. Worse, participants may be incorrectly valued as negative contribution under the Non-IID data scenarios, further jeopardizing fairness. In this work, we propose FairFed to address the above challenges. First, given that each iteration is of equal importance, FairFed treats FL as Multiple Single-stage Cooperative Games, and evaluates participants by each iteration for effectively coping with dynamic participants and ensuring fairness across iterations. Second, we introduce Cooperative Shapley Value (CSV) to rectify negative values of participants to improving the fairness while preserving true negative values. Third, we prove if participants are Strategically Equivalent, the number of participant combinations can be sharply reduced from exponential to polynomial, thus significantly reducing the computational complexity of CSV. Experimental results show that FairFed achieves up to 25.3 × speedup and reduces deviations by three orders of magnitude to two state-of-the-art approximation approaches.

Decentralized and open features of federated learning provides opportunities for malicious participants to inject stealthy trojan functionality into deep learning models collusively. A successful trojan attack is desired to be effective, precise and imperceptible, which generally requires priori knowledge such as aggregation rules, tight cooperation between attackers, e.g. sharing data distributions, and the use of inconspicuous triggers. However, in realistic, attackers are typically lack of the knowledge and hardly to fully cooperate (for privacy and efficiency reasons), and out of scope triggers are easy to be detected by scanners. We propose FedTrojan, a zero-knowledge federated trojan attack. Each attacker independently trains a quasi-trojaned local model with a self-select trigger. The model behaves normally on both regular and trojaned inputs. When local models are aggregated on the server side, the corresponding quasi-trojans will be assembled into a complete trojan which can be activated by the global trigger. We choose existing benign features rather than artificial patches as hidden local triggers to guarantee imperceptibility, and introduce catalytic features to eliminate the impact of local trojan triggers on behaviors of local/global models. Extensive experiments show that the performance of FedTrojan is significantly better than that of existing trojan attacks under both the classic FedAvg and Byzantine-robust aggregation rules.

Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients manipulate the global model by modifying local datasets or sending manipulated model updates. Experienced defenders can readily detect and mitigate the poisoning effects of malicious behaviors using Byzantine-robust aggregation rules. However, the exploration of poisoning attacks in scenarios where such behaviors are absent remains largely unexplored for Byzantine-robust FL. This paper addresses the challenging problem of poisoning Byzantine-robust FL by introducing catastrophic forgetting. To fill this gap, we first formally define generalization error and establish its connection to catastrophic forgetting, paving the way for the development of a clean-label data poisoning attack named BadSampler. This attack leverages only clean-label data (i.e., without poisoned data) to poison Byzantine-robust FL and requires the adversary to selectively sample training data with high loss to feed model training and maximize the model's generalization error. We formulate the attack as an optimization problem and present two elegant adversarial sampling strategies, Top-$\kappa$ sampling, and meta-sampling, to approximately solve it. Additionally, our formal error upper bound and time complexity analysis demonstrate that our design can preserve attack utility with high efficiency. Extensive evaluations on two real-world datasets illustrate the effectiveness and performance of our proposed attacks.

Large Vision Language Models (VLMs) extend and enhance the perceptual abilities of Large Language Models (LLMs).Despite offering new possibilities for LLM applications, these advancements raise significant security and ethical concerns, particularly regarding the generation of harmful content.While LLMs have undergone extensive security evaluations with the aid of red teaming frameworks, VLMs currently lack a well-developed one.To fill this gap, we introduce Arondight, a standardized red team framework tailored specifically for VLMs.Arondight is dedicated to resolving issues related to the absence of visual modality and inadequate diversity encountered when transitioning existing red teaming methodologies from LLMs to VLMs.Our framework features an automated multi-modal jailbreak attack, wherein visual jailbreak prompts are produced by a red team VLM, and textual prompts are generated by a red team LLM guided by a reinforcement learning agent.To enhance the comprehensiveness of VLM security evaluation, we integrate entropy bonuses and novelty reward metrics.These elements incentivize the RL agent to guide the red team LLM in creating a wider array of diverse and previously unseen test cases.Our evaluation of ten cutting-edge VLMs exposes significant security vulnerabilities, particularly in generating toxic images and aligning multi-modal prompts.In particular, our Arondight achieves an average attack success rate of 84.5% on GPT-4 in all fourteen prohibited scenarios defined by OpenAI in terms of generating toxic text.For a clearer comparison, we also categorize existing VLMs based on their safety levels and provide corresponding reinforcement recommendations.Our multimodal prompt dataset and red team code will be released after ethics committee approval.

The rapid growth of blockchain networks has led to a surge in lightweight client adoption, as full nodes become increasingly resource-intensive. This shift introduces two critical challenges: 1) Lightweight clients must rely on external sources for transaction data, exposing users to potential privacy breaches and security threats. 2) Existing privacy-preserving query sys-tems struggle to handle the vast amounts of data in blockchain networks efficiently. To tackle the dual challenges of maintaining query privacy and managing extensive blockchain data efficiently, our framework seamlessly integrates state-of-the-art Private Information Retrieval (PIR) techniques with Trusted Execution Environment (TEE) hardware capabilities. Our solution also sup-ports diverse query types, including keyword-based, predicate, and range queries, while enabling lightweight clients to verify the integrity and completeness of query results. Experimental results demonstrate that our approach reduces full-node storage overhead by nearly 50 % and improves communication efficiency by 4x compared to state-of-the-art PIR-based solutions, with only a modest 4–11 % increase in query time for integrity verification.

Face authentication has become prevalent in our daily lives. However, mainstream vision-based face authentication systems suffer from some limitations, including poor performance in dark environments, vulnerability to spoofing attacks, and privacy concerns. This paper proposes EchoLive, an acoustic-based 3D dynamic face authentication system that leverages acoustic sensors on mobile devices to sense the 3D dynamic facial cues for authentication. It utilizes the speaker to emit ultrasonic acoustic signals and the microphone to receive the echo signals from the user's face. From the echo signals, we can extract users' unique 3D facial geometry and dynamic facial information (i.e., head movements) and then perform reliable and secure 3D dynamic authentication. Utilizing acoustic sensing technology, EchoLive exhibits robustness under various environmental lighting and noise conditions, providing strong security guarantees and alleviating user concerns about the privacy of visual facial data. Extensive experiments show that EchoLive can realize reliable 3D dynamic face authentication and defend against mainstream spoofing attacks well.

Numerous studies have underscored the significant privacy risks associated with various leakage patterns in encrypted data stores. While many solutions have been proposed to mitigate these leakages, they either (1) incur substantial overheads, (2) focus on specific subsets of leakage patterns, or (3) apply the same security notion across various workloads, thereby impeding the attainment of fine-tuned privacy-efficiency trade-offs. In light of various detrimental leakage patterns, this paper starts with an investigation into which specific leakage patterns require our focus in the contexts of key-value, range-query, and dynamic workloads, respectively. Subsequently, we introduce new security notions tailored to the specific privacy requirements of these workloads. Accordingly, we propose and instantiate Swat, an efficient construction that progressively enables these workloads, while provably mitigating system-wide leakage via a suite of algorithms with tunable privacy-efficiency trade-offs. We conducted extensive experiments and compiled a detailed result analysis, showing the efficiency of our solution. Swat is about an order of magnitude slower than an encryption-only data store that reveals various leakage patterns and is two orders of magnitude faster than a trivial zero-leakage solution. Meanwhile, the performance of Swat remains highly competitive compared to other designs that mitigate specific types of leakage.

Encrypted databases have garnered considerable attention for their ability to safeguard sensitive data outsourced to third parties. However, recent studies have revealed the vulnerability of encrypted databases to leakage-abuse attacks on their search module, prompting the development of countermeasures to address this issue. While most studies have focused on static databases, limited research has been conducted on dynamic encrypted databases. To bridge this gap, this paper focuses on undertaking a comprehensive examination of leakage exploitation in dynamic encrypted databases, with the aim of providing effective mitigations. Our investigation begins with two attacks that can be employed to recover encrypted queries. The first attack, known as an active attack, involves injecting encoded files and utilizing correlated file volume information. The second attack, referred to as a passive attack, identifies unique relational characteristics of queries across database updates, assuming certain background knowledge of the plaintext databases. To mitigate these attacks, a two-layer encrypted database hardening approach is proposed, which obfuscates both search indexes and files in a continuous way. Doing so allows us to eliminate the unique characteristics emerging after data updates constantly. We conduct a series of experiments to confirm the severity of our attacks and the effectiveness of our countermeasures.

Mobile crowdsensing (MCS) has rapidly emerged as a popular paradigm for sensory data collection and benefited various location-based services and applications like road monitoring, smart transportation, and environmental monitoring. In practice, there often exist data-missing regions in the target sensing area, due to factors like limited budget, large area size, and scarcity of participants. This poses a demand for data recovery, which is commonly done based on the compressive sensing (CS) technique. However, CS-based data recovery requires access to sensory data tagged with locations, raising critical concerns on participants’ location privacy. While a plethora of location privacy techniques exist, most of them breach the data correlation inherently required by CS-based data recovery. Meanwhile, existing works mostly focus on protecting locations and overlook sensory data which may also indirectly lead to location leakages. In this paper, we propose SecDR, a new system design supporting secure, efficient, and accurate data recovery for location-based MCS applications. SecDR protects both locations and sensory data, and is built from a delicate synergy of CS-based data recovery and lightweight cryptography techniques. Extensive evaluations demonstrate that SecDR achieves promising performance and, even with stronger security guarantees, outperforms the state-of-the-art, with accuracy close to the plaintext domain.

Distributed Outsourced Storage systems, exemplified by the InterPlanetary File System (IPFS), offer compelling alternatives to traditional centralized cloud storage by emphasizing resilience and openness. Advancing this paradigm, Decentralized Storage (DS) markets leverage distributed ledgers to facilitate the monetization of outsourced storage. However, these markets often prioritize security over cost-efficiency, leading to high costs in existing DS markets. In our work, we introduce a middleware service, DWare, utilizing trusted hardware to balance security and cost efficiency. DWare offers two key advantages: 1) It enhances storage auditing efficiency by delegating computational tasks and standardizing the batched audit process. This approach offers a more feasible solution for validating outsourced storage with recurring pay-offs. 2) It implements secure and verifiable data deduplication, thereby increasing storage efficiency and reducing operational costs. This step, commonplace in cloud storage services, remains largely unexplored in current DS designs. While DWare could empirically reduce costs to levels near raw storage fees, it entails certain security concessions due to middleware involvement. To address this, we propose a hybrid trust security model, granting data owners the flexibility to adjust the security-cost balance as needed.

Machine unlearning as a fundamental requirement in Machine-Learning-as-a-Service (MLaaS) has been extensively studied with increasing concerns about data privacy. It requires MLaaS providers should delete training data upon user requests. Unfortunately, none of the existing studies can efficiently achieve machine unlearning validation while preserving the retraining efficiency and the service quality after data deletion. Besides, how to craft the validation scheme to prevent providers from spoofing validation by forging proofs remains under-explored. In this paper, we introduce a backdoor-assisted validation scheme for machine unlearning. The proposed design is built from the ingenious combination of backdoor triggers and incremental learning to assist users in verifying proofs of machine unlearning without compromising performance and service quality. We propose to embed invisible markers based on backdoor triggers into privacy-sensitive data to prevent MLaaS providers from distinguishing poisoned data for validation spoofing. Users can use prediction results to determine whether providers comply with data deletion requests. Besides, we incorporate our validation scheme into an efficient incremental learning approach via our index structure to further facilitate the performance of retraining after data deletion. Evaluation results on real-world datasets confirm the efficiency and effectiveness of our proposed verifiable machine unlearning scheme.

Recent advancements in adversarial attack research have seen a transition from white-box to black-box and even no-box threat models, greatly enhancing the practicality of these attacks. However, existing no-box attacks focus on instance-specific perturbations, leaving more powerful universal adversarial perturbations (UAPs) unexplored. This study addresses a crucial question: can UAPs be generated under a no-box threat model? Our findings provide an affirmative answer with a texture-based method. Artificially crafted textures can act as UAPs, termed Texture-Adv. With a modest density and a fixed budget for perturbations, it can achieve an attack success rate of 80% under the constraint of$l_{\infty }$= 10/255. In addition, Texture-Adv can also take effect under traditional black-box threat models. Building upon a phenomenon associated with dominant labels, we utilize Texture-Adv to develop a highly efficient decision-based attack strategy, named Adv-Pool. This approach creates and traverses a set of Texture-Adv instances with diverse classification distributions, significantly reducing the average query budget to less than 1.3, which is near the 1-query lower bound for decision-based attacks. Moreover, we empirically demonstrate that Texture-Adv, when used as a starting point, can enhance the success rates of existing transfer attacks and the efficiency of decision-based attacks. The discovery suggests its potential as an effective starting point for various adversarial attacks while preserving the original constraints of their threat models.

Cloud computing has been a research focus in both academic and industrial communities for decades. Along with this trend, Searchable Encryption (SE) technology emerged and developed as data privacy concerns increased. Many schemes are proposed to solve the privacy-preserving data-sharing problem in multi-user scenarios. Most existing solutions are based on the assumption that all users are trusted. However, there will be cross-user leakage when there are malicious or compromised ones. This is because of the inherent linkability of authorization information and the search result when multiple users request data from the same database. To this end, we propose a cross-user leakage mitigation scheme for authorized encrypted data sharing in a two-server model. We utilize a blinding factor to delink authorizations based on Symmetric Multi-Key Searchable Encryption (SMKSE). To break the linkability of query results, we combine the zero-sum garbled Bloom filter with the oblivious transfer technique, where each of the two servers can only know partial information. We devise a group-based Bloom filter structure in indices to improve efficiency. We perform formal security analysis and also demonstrate the efficiency through comparative experiments.

It is increasingly common for enterprises/organizations to outsource graph analytics services to the cloud. For example, enterprises may leverage the cloud to store and query large attributed graphs. Among others, subgraph matching over a large attributed graph is a common and fundamental query functionality for graph analytics. It aims to retrieve all isomorphic subgraphs for a small query graph and greatly benefits various application domains like cheminformatics, social network analysis, and anti-money laundering. Deploying subgraph matching service in the cloud, however, poses a threat to the privacy of the information-rich graph data as the cloud gains access to the attributed graph, query graph, and query result. Given this, several works have been proposed for supporting privacy-aware subgraph matching. However, prior works only consider a weak semi-honest threat model and cannot provide integrity guarantees for the subgraph matching results in case of malicious adversary. In light of this, we design, implement, and evaluate eGrass, a new system enabling maliciously secure attributed subgraph matching service outsourced to the cloud. In addition to offer protection for graph data confidentiality, eGrass is also designed to hide search access patterns as well as defend against malicious cloud server that attempt to compromise the result integrity. We conduct extensive experiments on a real-world dataset. The results demonstrate that compared to the state-of-the-art previous protocol with semi-honest security, eGrass is only 3 × −4.7× slower in query latency, uses 3 × −3.5× more communication, and does not require extra cloud-side storage.

The “Right to be Forgotten” rule in machine learning (ML) practice enables some individual data to be deleted from a trained model, as pursued by recently developed machine unlearning techniques. To truly comply with the rule, a natural and necessary step is to verify if the individual data are indeed deleted after unlearning. Yet, previousparameter-spaceverification metrics may be easily evaded by a distrustful model trainer. Thus, Thudiet al. recently present a call to action onalgorithm-levelverification in USENIX Security’22. We respond to the call, by reconsidering the unlearning problem in the scenario of machine learning as a service (MLaaS), and proposing a new definition framework forProof of Unlearning(PoUL) on algorithm level. Specifically, our PoUL definitions (i) enforce correctness properties on both the pre and post phases of unlearning, so as to prevent the state-of-the-art forging attacks; (ii) highlight proper practicality requirements of both the prover and verifier sides with minimal invasiveness to the off-the-shelf service pipeline and computational workloads. Under the definition framework, we subsequently present a trusted hardware-empowered instantiation using SGX enclave, by logically incorporating an authentication layer for tracing the data lineage with a proving layer for supporting the audit of learning. We customize authenticated data structures to support large out-of-enclave storage with simple operation logic, and meanwhile, enable proving complex unlearning logic with affordable memory footprints in the enclave. We finally validate the feasibility of the proposed instantiation with a proof-of-concept implementation and multi-dimensional performance evaluation.

Decentralized storage platforms based on consortium blockchains have emerged in the spotlight of research and industry communities because they are flexible, transparent, and eliminated trust in contrast to the traditional centralized data-sharing model. However, due to wide attacking surfaces in a blockchain network, this decentralized data-sharing paradigm is subject to malicious data breaches. Untrusted blockchain nodes can directly obtain sensitive information from the query processing and their local storage. Several studies have been made for solving this dilemma, but they only focus on single-user settings and cannot be directly applied to multi-owners blockchain-based data sharing scenarios. In this paper, we introduce FedEDB, a federated and encrypted data store by using consortium blockchains. Unlike existing solutions that focus on single-user settings, our proposed schemes can efficiently support privacy-preserving and reliable multi-owner queries in the decentralized setting. We start from the practical key aggregation technique to construct the multi-owner search schemes and further refine the underling building blocks to enhance the security. Besides, we integrate the smart contract with our tailored zero-knowledge proof to enforce secure and reliable result verification protocol with fairness. We implement a prototype and thorough security analysis and comprehensive evaluation results confirm the practicability of our design.

Recent advances in cryptocurrencies have sparked significant interest in blockchain technology. However, scalability issues remain a major challenge for wide adoption of blockchains. Sharding is a promising approach to scale blockchains, but existing sharding-based blockchains fail to achieve expected performance gains due to limitations in cross-shard transaction processing. In this paper, we propose X-shard, a blockchain system that optimizes cross-shard transaction processing, achieving high effective throughput and low processing latency. First, we allocate transactions to shards based on historical transaction patterns to minimize cross-shard transactions. Second, we take an optimistic strategy to process cross-shard transactions in parallel as sub-transactions within input shards, thereby accelerating transaction processing. Finally, we employ a cross-shard commit protocol with threshold signatures to reduce communication overhead. We implement and deploy X-shard on Amazon EC2 clusters. Experimental results validate our theoretical analysis and show that as the number of shards increases, X-shard achieves nearly linear scaling in effective throughput and decreases in transaction processing latency.

Secure deduplication over encrypted data can greatly improve cloud storage efficiency and protect data privacy. Recently, there have been some research efforts aiming at designing secure deduplication schemes with the assistance of key servers (KSs). However, prior works are unsatisfactory in that they suffer from some limitations such as security degradation (the leakage at partial KSs will lead to all the ciphertexts being subject to offline brute-force attacks) or lack of scalability for handling the change of KSs. In this paper, we propose a new secure deduplication scheme for large-scale cloud storage service, which, to our best knowledge, is the first server-aided scheme that supports both tolerance of partial KSs leakage and dynamic change of KSs. Our scheme divides all the KSs into multiple groups and each KS group keeps a randomly generated secret key using threshold cryptography. We design a file-related KS group selection mechanism for assisting encryption key generation, which guarantees that the identical files of different users can be encrypted using the same keys. Our scheme is designed to update the KS groups regularly for supporting the joining and leaving of the KSs as well as maintaining long-term security. We leverage the blockchain to help divide KSs into groups in a fair way and securely migrate group secret keys during KS group updating. Formal analysis is provided to verify the correctness of our scheme and justify its security, and both theoretical and experimental results demonstrate that it has modest performance overhead.

Enforcing end-to-end data encryption is vital for protecting the interests of requesters in crowdsourcing services, who initiate crowdsourcing tasks and need to pay the service provider and reward workers for the crowdsourced data. It ensures that the data encrypted by workers can be decrypted by the requester only. This yet makes it challenging to protect workers in getting rewards as the data is now only accessible to the requester, who may falsely report workers’ data quality. There is thus an urgent call for enforcing end-to-end data encryption while achieving robustness against such false-reporting. However, this is not yet sufficient for worker protection because most platforms with quality awareness involve a screening process for worker selection in advance, which requires collecting personal worker profiles for assessment against task requirements and raises privacy concerns. In light of the above, we propose PrivRo, a new system framework for privacy-preserving crowdsourcing service with robust quality awareness. PrivRo supports private profile matching for secure screening as well as secure data collection with verifiable quality reporting through custom secure protocols. To our best knowledge, no prior work has simultaneously and adequately supported the secure functionalities compared to PrivRo. Extensive experiments demonstrate the practical efficiency of PrivRo.

Metadata-private messaging (MPM) refers to an end-to-end encrypted messaging system that protects not just the payload messages but also the privacy-revealing communication metadata, such as user identities, conversation frequencies, traffic volumes, etc. Protecting the communication metadata is challenging due to the existence of global adversaries that can monitor and even actively interfere with the traffic. Established systems like Tor are not adequate under such adversarial models. Thus, many academic systems have been proposed to push this frontier with different trade-offs among security, performance, and trust assumptions. Despite progress, one major limitation prevalent in almost all prior art is the requirement for messaging buddies to coordinate the time (also known as "dialing'') to start the conversation. Compared to traditional messaging systems, such coordination protocols, which must also be metadata private, are expensive for both user adoption and service operations. In this ongoing study, we propose to develop a new MPM system without coordination. Unlike prior art, we plan to model the MPM system into two separate modules: metadata-private notifications and metadata-private message retrieval, which is intuitively inspired by traditional messaging systems. We will instantiate these ideas by drawing insights from recent work about private signaling, oblivious message retrieval, and MPM under hardware trust.

This work presents HPCG (short for hardware-assisted pseudorandom correlation generator), a work-in-progress lightweight TEE (LTEE)-assisted MPC solution for both high performance and strong security. HPCG relies on a succinct codebase and small LTEE chips that work only in the MPC offline phase, with the aim of addressing efficiency bottlenecks in traditional MPC, while minimizing the use and trust in secure hardware, which makes a rational compromise between pure cryptography and TEE techniques. We design HPCG to work for diverse MPC settings in the preprocessing model and conform to the mainstream secret-sharing semantics, making it easy to deploy and integrate into existing MPC practices.

Dynamic searchable symmetric encryption (DSSE) enables a server to efficiently search and update over encrypted files. To minimize the leakage during updates, a security notion named forward and backward privacy is expected for newly proposed DSSE schemes. Those schemes are generally constructed in a way to break the linkability across search and update queries to a given keyword. However, it remains underexplored whether forward and backward private DSSE is resilient against practical leakage-abuse attacks (LAAs), where an attacker attempts to recover query keywords from the leakage passively collected during queries.

Large language models, such as OpenAI's Chat-GPT, have demonstrated exceptional language understanding capabilities in various NLP tasks.Sparsely activated mixture-of-experts (MoE) has emerged as a promising solution for scaling models while maintaining a constant number of computational operations.Existing MoE model adopts a fixed gating network where each token is computed by the same number of experts.However, this approach contradicts our intuition that the tokens in each sequence vary in terms of their linguistic complexity and, consequently, require different computational costs.Little is discussed in prior research on the tradeoff between computation per token and model performance.This paper introduces adaptive gating in MoE, a flexible training strategy that allows tokens to be processed by a variable number of experts based on expert probability distribution.The proposed framework preserves sparsity while improving training efficiency.Additionally, curriculum learning is leveraged to further reduce training time.Extensive experiments on diverse NLP tasks show that adaptive gating reduces at most 22.5% training time while maintaining inference quality.Moreover, we conduct a comprehensive analysis of the routing decisions and present our insights when adaptive gating is used.

Organizations often build separate training and inference clusters for deep learning, and use separate schedulers to manage them. This leads to problems for both: inference clusters have low utilization when the traffic load is low; training jobs often experience long queuing due to a lack of resources. We introduce Lyra, a new cluster scheduler to address these problems. Lyra introduces capacity loaning to loan idle inference servers for training jobs. It further exploits elastic scaling that scales a training job's resource allocation to better utilize loaned servers. Capacity loaning and elastic scaling create new challenges to cluster management. When the loaned servers need to be returned, we need to minimize job preemptions; when more GPUs become available, we need to allocate them to elastic jobs and minimize the job completion time (JCT). Lyra addresses these combinatorial problems with principled heuristics. It introduces the notion of server preemption cost, which it greedily reduces during server reclaiming. It further relies on the JCT reduction value defined for each additional worker of an elastic job to solve the scheduling problem as a multiple-choice knapsack problem. Prototype implementation on a 64-GPU testbed and large-scale simulation with 15-day traces of over 50,000 production jobs show that Lyra brings 1.53x and 1.48x reductions in average queuing time and JCT, and improves cluster usage by up to 25%.

Encrypted range schemes enable the user to perform expressive range queries over encrypted databases by only revealing the necessary information for the search. In comparison to the extensively studied keyword schemes, range schemes inherently carry a more detrimental leakage profile as they contain extra structural information that can be exploited by attackers. This is evidenced by the escalation of attacks and defenses targeting range schemes in recent years, showing an inadequate understanding of range schemes' leakage implications. To this end, we identify and investigate two major aspects that have been largely overlooked in prior leakage analysis: 1) lack of a comprehensive approach for coordinating access, volume, and search patterns simultaneously, resulting in separate resolutions for each type of leakage; 2) absence of granular leakage quantification aimed at individual queries, leading to the gradual accumulation of imperceptible leakage over time. Based on this understanding, we present the query control framework RangeQC that provides a novel uniformed viewpoint of all three primary patterns at a per-query level. At the core of our techniques is a set of customized entropy analyzes tailored for the representative structures extracting various types of patterns. We further develop feasible countermeasures to suppress pattern leakage with tunable query control rules. Extensive evaluation results on real-world datasets demonstrate the superiority of RangeQC for leakage quantification and mitigation effectiveness.

Self-supervised learning (SSL) has been recently applied to sequential recommender systems to provide high-quality user representations. However, while facilitating the learning process recommender systems, SSL is not without security threats: carefully crafted inputs can poison the pre-trained models driven by SSL, thus reducing the effectiveness of the downstream recommendation model. This work shows that poisoning attacks against the pre-training stage threaten sequential recommender systems. Without any background knowledge of the model architecture and parameters, nor any API queries, our strategy proves the feasibility of poisoning attacks on mainstream SSL-based recommender schemes as well as on commonly used datasets. By injecting only a tiny amount of fake users, we get the target item recommended to real users more than thousands of times as before, demonstrating that recommender systems have a new attack surface due to SSL. We further show our attack is challenging for recommendation platforms to detect and defend. Our work highlights the weakness of self-supervised recommender systems and shows the necessity for researchers to be aware of this security threat. Our source code is available at https://github.com/CongGroup/Poisoning-SSL-based-RS.

Recently more and more cloud service providers (e.g., Microsoft, Google, and Amazon) have commercialized their well-trained deep learning models by providing limited access via web API interfaces. However, it is shown that these APIs are susceptible to model inversion attacks, where attackers can recover the training data with high fidelity, which may cause serious privacy leakage.Existing defenses against model inversion attacks, however, hinder the model performance and are ineffective for more advanced attacks, e.g., Mirror [4]. In this paper, we proposed NetGuard, a novel utility-aware defense methodology against model inversion attacks (MIAs). Unlike previous works that perturb prediction outputs of the victim model, we propose to mislead the MIA effort by inserting engineered fake samples during the training process. A generative adversarial network (GAN) is carefully built to construct fake training samples to mislead the attack model without degrading the performance of the victim model. Besides, we adopt continual learning to further improve the utility of the victim model. Extensive experiments on CelebA, VGG-Face, and VGG-Face2 datasets show that NetGuard is superior to existing defenses, including DP [37] and Ad-mi [32] on state-of-the-art model inversion attacks, i.e., DMI [8], Mirror [4], Privacy [12], and Alignment [34].

It is increasingly popular for cloud providers to offer middlebox service that supports content-based similarity detection for enterprises. However, redirecting network traffic to the cloud for such service raises security concerns. While trusted execution environments such as Intel SGX have emerged as a pragmatic solution for designing secure in-the-cloud middleboxes, it remains challenging to practically support content-based similarity detection. In this paper, we design a secure in-the-cloud middlebox system that can detect content-based similar flows in encrypted traffic dynamically. To cope with the constrained enclave memory, we adopt the caching technique and devise a compact index to increase the cache hit rate for effective similarity detection inside the enclave. We also present a parallel algorithm for performance speedup, with an efficient enclave thread management mechanism. Extensive evaluations demonstrate that the overhead of our system compared to native processing (without SGX) is limited to 2.1×. Meanwhile, our tailored design can achieve up to$14.4\times$better computational efficiency compared to simply moving the target functionality to the SGX enclave via existing popular library operating systems like Graphene-SGX and Occlum. Our secure system can achieve a normalized similarity detection precision of about 90%.

To keep web users away from unsafe websites, modern web browsers enable the embedded feature of safe browsing (SB) by default. In this work, through theoretical analysis and empirical evidence, we reveal two major shortcomings in the current SB infrastructure. First, we derive a feasible tracking technique for industry best practice. We show that the current mitigation techniques cannot eliminate the threat of de-anonymization permanently. Second, we gauge the effectiveness of blacklists provided by major vendors. Our discovery indicates the urge for blacklist integration in order to boost service quality. In light of this, we propose a new three-party paradigm${{\sf PEBA}}$with an intermediate third party decoupling the direct interaction of users and proprietary blacklist vendors. To satisfy practical usage requirements, we instantiate our design with trusted hardware, detailing how it can be leveraged to fulfill the requirements of privacy enhancement and broader content coverage at the same time. We also tackle numerous implementation challenges that emerged from this proxy-based and hardware-enabled solution. Extensive evaluation confirms that${{\sf PEBA}}$can balance well among desirable goals of security, usability, performance, and elasticity, making it suitable for deployment in practice.

Decentralized storage (DS) projects such as Filecoin are gaining traction. Their openness mandates effective auditing mechanisms to assure users that their data remains intact. A blockchain is typically employed here as an unbiased public auditor. While the case for static data is relatively easy to handle, on-chain auditing of dynamic data with practical performance guarantees is still an open problem. Dynamic Proof-of-Storage (PoS) schemes developed for conventional cloud storage are not applicable to DS, since they require large storage proofs and/or large auditor states that are unmanageable by a resource-constrained blockchain. To fill the gap, we propose a family of dynamic on-chain auditing protocols that can produce concretely small auditor states while retaining the compact proofs promised by static PoS schemes. Our design revolves around a set of succinct data structures and optimization techniques for index information management. With proper instantiation and realistic parameters, our protocols can achieve 0.25MB on-chain state and 1.2KB storage proof for the auditing of 1TB data, outperforming previous dynamic PoS schemes that are adaptable for DS by orders of magnitude. As another practical contribution, we introduce a data abstraction layer that allows one to deploy the auditing protocols on arbitrary storage systems hosting dynamic data.

Dynamic Searchable Symmetric Encryption (DSSE) is a practical cryptographic primitive that assists servers to provide search and update functionalities in the ciphertext domain. Recent work on DSSE schemes has focused on the direction of forward-privacy, requiring that newly added files cannot be linked to previously query results. However, due to the complexity of forward-privacy updates, existing schemes can only address an honest-but-curious server. It is difficult to verify updated results while preserving forward-privacy. In this paper, we explore how blockchain techniques can help us achieve a verifiable and forward-privacy DSSE scheme. Our scheme resorts to the emerging smart contract as a trusted platform to store digests for public result verification, and carefully crafts dynamic query protocols to enable encrypted search with forward-privacy. In our design, indexes are collocated with encrypted files and stored at storage-servers, which makes the blockchain light-weighted and search operations more efficient. Moreover, we propose a hybrid index design to support efficient files deletion. By using our blockchain-assisted primitive, the property collision between dynamic result verification and forward-privacy can be solved. We formally analyze the security strengths and provide the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of our blockchain-assisted DSSE scheme.

Voice authentication has been increasingly adopted for sensitive operations on mobile devices. While voice biometrics can distinguish individuals by their spectral features (such as voiceprints), they are known to be prone to spoofing attacks, where malicious attackers can use pre-recorded or synthesized samples from legitimate users or impersonate the speaking style of the targeted user to deceive the voice authentication system. In this paper, we design and implement a novel software-only anti-spoofing system on smartphones. Our system leverages thepop noise, which is generated by the user’s oral airflow when speaking the passphrase close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to be recorded by replay attacks beyond a certain distance or to be imitated precisely by impersonators. Specifically, we design a new pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish a theoretical model to calculate the sound pressure level from the speech signal in order to get the estimated pressure signal, and then analyze the consistency with the actual pressure signal extracted from the pop noise. Furthermore, we calculate the similarity score of the unique sequences which describe the individually unique relationship between pop noises and phonemes to resist spoofing attacks. Our evaluation on a dataset of 30 participants and three smartphones shows that our system achieves over 94.79% accuracy. Our system requires no additional hardware and is robust to various factors including authentication angle, authentication distance, the length of passphrase, ambient noise, etc.

Mobile two-factor authentication (TFA), which uses mobile devices as a second security layer of protection to online accounts, has been widely applied with the proliferation of mobile phones. Currently, many studies propose to use acoustic fingerprints as the second factor. However, these solutions ignore the variations of the extracted static acoustic fingerprints incurred by the acoustic propagation process, which we show can be leveraged to develop an enhanced man-in-the-middle (MITM) attack to compromise the security strength of these systems, while hiding the traces of the attacking devices. To address this newly-uncovered vulnerability, we propose SoundID, a secure and novel authentication system that introduces a dual challenge-response design through the acoustic signals of the enrolled phone and the login device. Specifically, the enrolled phone first evaluates its proximity to the login device by the similarity of their audio recordings, and then the login authentication server compares the calculated dynamic acoustic fingerprint with the one received from the enrolled phone. To the best of our knowledge, SoundID is the first scheme that extracts dynamic acoustic fingerprints and can effectively defend against the enhanced MITM attack. SoundID combines the benefits of unpredictable influencing factors of acoustic propagation processes and the stable frequency response of the acoustic hardware, whose high complexity prevents attackers from predicting or impersonating them. We build a prototype of SoundID with off-the-shelf smartphones to validate its robustness and effectiveness. Our results show that SoundID is user-friendly and achieves over 96.62% accuracy with an equal error rate around 4.27%.

Transfer learning is prevalent as a technique to efficiently generate new models (Student models) based on the knowledge transferred from a pre-trained model (Teacher model). However, Teacher models are often publicly available for sharing and reuse, which inevitably introduces vulnerability to trigger severe attacks against transfer learning systems. In this article, we take a first step towards mitigating one of the most advanced misclassification attacks in transfer learning. We design a distilleddifferentiatorvia activation-based network pruning to enervate the attack transferability while retaining accuracy. We adopt an ensemble structure from variant differentiators to improve the defence robustness. To avoid the bloated ensemble size during inference, we propose a two-phase defence, in which inference from the Student model is first performed to narrow down the candidate differentiators to be assembled, and later only a small, fixed number of them can be chosen to validate clean or reject adversarial inputs effectively. Our comprehensive evaluations on both large and small image recognition tasks confirm that the Student models with our defence of only 5 differentiators are immune to over 90% of the adversarial inputs with an accuracy loss of less than 10%. Our comparison also demonstrates that our design outperforms prior problematic defences.

Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only model updates are locally computed and shared for aggregation to produce a global model. While federated learning greatly alleviates the privacy concerns as opposed to learning with centralized data, sharing model updates still poses privacy risks. In this paper, we present a system design which offers efficient protection of individual model updates throughout the learning procedure, allowing clients to only provide obscured model updates while a cloud server can still perform the aggregation. Our federated learning system first departs from prior works by supporting lightweight encryption and aggregation, and resilience against drop-out clients with no impact on their participation in future rounds. Meanwhile, prior work largely overlooks bandwidth efficiency optimization in the ciphertext domain and the support of security against an actively adversarial cloud server, which we also fully explore in this paper and provide effective and efficient mechanisms. Extensive experiments over several benchmark datasets (MNIST, CIFAR-10, and CelebA) show our system achieves accuracy comparable to the plaintext baseline, with practical performance.

Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the private input data of the client. In this paper, we design, implement, and evaluate a new system that allows highly efficient outsourcing of decision tree inference. Our system significantly improves upon prior art in the overall online end-to-end secure inference service latency at the cloud as well as the local-side performance of the model provider. We first present a new scheme which securely shifts most of the processing of the model provider to the cloud, resulting in a substantial reduction on the model provider's performance complexities. We further devise a scheme which substantially optimizes the performance for secure decision tree inference at the cloud, particularly the communication round complexities. The synergy of these techniques allows our new system to achieve up to$8 \times$better overall online end-to-end secure inference latency at the cloud side over realistic WAN environment, as well as bring the model provider up to$19 \times$savings in communication and$18 \times$savings in computation.

PIN authentication is widely used on mobile devices due to its usability and simplicity. However, it is known to be susceptible to shoulder surfing attacks, where an adversary spies the user’s PIN by direct human observation or camera-based recording. This paper proposes PressPIN, a novel enhanced PIN authenticator on mobile devices by sensing pressures from the user’s finger. Since pressure-sensitive touch screens are unavailable on most phones, we leverage the structure-borne propagation of sounds to estimate the pressure on the screen. When the user inputs the PINs, the pressure is extracted from each number to form the$n$-bit pressure code, where$n$corresponds to the length of the PIN sequence. The pressure code is difficult to be inferred by snooping or videotaping, and increases the entropy of passwords. In this way, PressPIN provides a low-cost, user-friendly, and more secure solution resistant to shoulder surfing attacks. Our extensive experiments with 30 participants and three types of smartphones demonstrate that PressPIN can authenticate legitimate users with high accuracy (e.g., as high as 96.7% within two trials), and is robust to various types of attacks (e.g., only 2.5% attack success rate even when the adversary can observe the legitimate user’s PIN sequence and finger pressing clearly). Additionally, PressPIN requires no additional hardware (e.g., the pressure sensor) and can be readily integrated into existing authentication systems of mobile devices.

With the ubiquitous adoption, mobile face authentication systems have been facing constant security challenges, particularly the spoofing risks. Except for those using specialized hardware, existing proposals for face anti-spoofing on mainstream smartphones either leverage people’s 3D face characteristics or various facial expressions. While showing progress towards more resilient face authentication, they are still vulnerable to recent advanced attacks (e.g., 3D mask attacks, video attacks, etc.). This paper presents GazeGuard, an on-device face anti-spoofing system that leverages unpredictable and unforgeable eye movement patterns to provide strong security guarantees against all known attacks. Targeting mainstream smartphones, GazeGuard is designed to conduct eye movement-based authentication using only 2D front cameras. Specifically, by presenting a series of short-lasting random dots on the screen (named gazecode), GazeGuard simultaneously captures a user’s gaze responses and the corresponding deformed periocular features to ensure both the freshness and correctness for the anti-spoofing face authentication. We have extensively tested GazeGuard’s performance over 50 volunteers. Using a 4-digit gazecode (just four random dots), GazeGuard achieves an average 90.39% authentication accuracy and 81.57 out of 100 System Usability Scale (SUS) scores. Under the same settings, GazeGuard achieves detection accuracy of 95.72% for image attack, 95.59% for video attack, 99.73% for 3D mask attack, and 100% for physical adversarial attack.

Cloud storage systems have seen a growing number of clients due to the fact that more and more businesses and governments are shifting away from in-house data servers and seeking cost-effective and ease-of-access solutions. However, the security of cloud storage is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of ShieldDB, an encrypted document database. ShieldDB adapts the searchable encryption technique to preserve the search functionality over encrypted documents without having much impact on its scalability. However, merely realising such a theoretical primitive suffers from real-world threats, where a knowledgeable adversary can exploit the leakage (aka access pattern to the database) to break the claimed protection on data confidentiality. To address this challenge in practical deployment, ShieldDB is designed with tailored padding countermeasures. Unlike prior works, we target a more realistic adversarial model, where the database gets updated continuously, and the adversary can monitor it at an (or multiple) arbitrary time interval(s). ShieldDB’s padding strategies ensure that the access pattern to the database is obfuscated all the time. We present a full-fledged implementation of ShieldDB and conduct intensive evaluations on Azure Cloud.

Graphs are widely used to model complex structured data in many applications. With the proliferation of cloud computing, it is popular to store and query graphs in the cloud. Among others, graph similarity search, which aims to retrieve from a graph database graphs similar to a query graph, has received wide attentions and benefited various domains such as cheminformatics, computer vision, and more. Deploying graph similarity search services on the cloud, however, raises critical privacy concerns on the information-rich graphs. In this article, we initiate the first study on privacy-preserving graph similarity search in cloud computing. We design, implement, and evaluate PrigSim, a novel system allowing the cloud to host an outsourced encrypted graph database and support secure graph similarity search, where the graph similarity is measured by the well-known metric called graph edit distance. PrigSim is built from a customized and delicate synergy of insights on graph modelling, lightweight cryptography, and data encoding and padding, providing protections for the confidentiality of data content associated with graphs, as well as hiding the connections among vertices. Extensive experiments demonstrate that the security design of PrigSim is accuracy-preserving, and presents modest performance overheads (with$9\times$-$15\times$higher query latency than the plaintext baseline).

The well-known benefits of cloud computing have spurred the popularity of database service outsourcing, where one can resort to the cloud to conveniently store and query databases. Coming with such popular trend is the threat to data privacy, as the cloud gains access to the databases and queries which may contain sensitive information, like medical or financial data. A large body of work has been presented for querying encrypted databases, which has been mostly focused on secure keyword search. In this paper, we instead focus on the support for secure skyline query processing over encrypted outsourced databases, where little work has been done. Skyline query is an advanced kind of database query which is important for multi-criteria decision-making systems and applications. We propose SecSkyline, a new system framework building on lightweight cryptography for fast privacy-preserving skyline queries. SecSkyline ambitiously provides strong protection for not only the content confidentiality of the outsourced database, the query, and the result, but also for data patterns that may incur indirect data leakages, such as dominance relationships among data points and search access patterns. Extensive experiments demonstrate that SecSkyline is substantially superior to the state-of-the-art in query latency, with up to$813\times$improvement.

With the rapid development of cloud computing, it is popular for data owners to outsource massive data to the cloud server for data sharing. To protect the privacy of sensitive data, many searchable encryption schemes are proposed. However, most of the existing studies focus on the single-owner model. In practice, users need to query data from distributed owners one by one, which inevitably brings great communication and computation overheads. Moreover, it lacks a secure scheme that realizes the access control requirements of individual owners. In this article, we propose AESM$^{2}$, a new attribute-based encrypted search with ownership enhancement scheme for multi-owner and multi-user distributed systems. Our design enables users to search data from authorized owners with only one trapdoor. Owners can enforce owner level permission on users and encrypt their data individually with fine-grained attribute level permission. For practical consideration, we further devise an efficient revocation method of the owner level permission for users, where ciphertexts do not need to be updated. We formally define and prove the security of our design. Moreover, we implement a system prototype and analyze the performance from theoretical and experimental aspects. The evaluation results demonstrate that our scheme is effective and efficient.

Although existing schemes improve blockchain throughput by allowing concurrent blocks to be appended to the blockchain, little attention has been devoted to adjusting blockchain throughput dynamically and deduplicating transactions between concurrent blocks. In this article, we propose AdaptChain, an adaptive scaling blockchain with transaction deduplication. When the transaction demand of users in the network is high, the blockchain expands to meet the demand; when the transaction demand is low, the blockchain shrinks to save communication and storage costs. Our transaction deduplication mechanism ensures that no duplicate transactions are added to the blockchain, thereby improving bandwidth utilization and achieving higher effective throughput. Besides, we randomly split the mining power of the system to achieve mining power load balancing and resist attacks. We formally analyze the blockchain security and implement the proposed prototype on Amazon EC2. Experimental results show that AdaptChain achieves dynamic and higher effective blockchain throughput.

Electronic Health Record (EHR) and its privacy have attracted widespread attention with the development of the healthcare industry in recent years. As locking medical data in a single healthcare center causes information isolation, healthcare centers are motivated to build medical data sharing systems. However, existing systems highly rely on the trusted centralized servers, which are vulnerable to distributed denial of service (DDoS) attacks and the single point of failure. Moreover, it is a non-trivial matter to authorize multiple users to search and access EHR in a privacy-preserving manner. In this paper, we propose MedShare, a decentralized framework for secure EHR sharing. Our design utilizes the smart contract technique of blockchain to establish a trusted platform for healthcare centers to share their encrypted EHR. Considering that fine-grained access control is essential in practical EHR sharing service, we devise a constant-size attribute-based encryption (ABE) scheme, where the access policy is embedded in search result on the blockchain. Besides, we propose an efficient scheme that enables authorized MedShare users to perform multi-keyword boolean search operations over encrypted EHR. We formally analyze the security strengths and implement the system prototype on Ethereum. Evaluation results demonstrate that MedShare is efficient for EHR sharing.

Edge computing brings data computation and storage closer to the mobile device to save response time for decision making. After being processed at the edge, commonly, the data will be uploaded to the cloud for further enriched analysis. For privacy concerns, local devices may encrypt the collected data before sending it to the cloud server. However, this treatment increases the servers processing time and makes it hard to pick out the desired data. In this paper, to address this problem, we design an encrypted data validation scheme, which enables the edge to clean the encrypted data to be uploaded. Because edge computing encompasses numerous edge devices from different service providers, we explore the public-key cryptographic mechanism to implement our secure data validation scheme. Considering potential risks from quantum computers, we propose to leverage ideal lattice to realize our protocol, which reaches better performance in both time and storage for edge devices. Extensive evaluation results show that our proposed proposal achieves considerable performance improvements in terms of communication and computation aspects. Particularly, compared to prior work, nearly 123x-238x speed up is achieved in our key derivation procedure and the storage cost of the secret key is reduced from 547MB to 1.7MB.

Owner-centric control is a widely adopted method for easing owners' concerns over data abuses and motivating them to share their data out to gain collective knowledge. However, while many control enforcement techniques have been proposed, privacy threats due to the metadata leakage therein are largely neglected in existing works. Unfortunately, a sophisticated attacker can infer very sensitive information based on either owners' data control policies or their analytic task participation histories (e.g., participating in a mental illness or cancer study can reveal their health conditions). To address this problem, we introduce Vizard, a metadata-hiding analytic system that enables privacy-hardened and enforceable control for owners. Vizard is built with a tailored suite of lightweight cryptographic tools and designs that help us efficiently handle analytic queries over encrypted data streams coming in real-time (like heart rates). We propose extension designs to further enable advanced owner-centric controls (with AND, OR, NOT operators) and provide owners with release control to additionally regulate how the result should be protected before deliveries. We develop a prototype of Vizard that is interfaced with Apache Kafka, and the evaluation results demonstrate the practicality of Vizard for large-scale and metadata-hiding analytics over data streams.

The flourishing development of blockchain and cryptocurrency has made it a hotbed for cyber-criminals to implement virtually untraceable scams. Consequently, the blockchain ecosystem urgently needs an effective method to help users stay away from scams in order to create an enticing investment environment. Despite the massive deployment of blocklist query APIs for malicious and scam domains/URLs in the industry, we identify two core reasons why existing blocklist services find it difficult to thrive in the cryptocurrency paradigm: 1) the compelling need to protect a user query due to sensitivity and high value of query content, i.e., payment addresses; 2) the thorny issue of evaluating the quality of blocklists effectively, in the face of common practices of incompetent providers.To this end, we first provide a private and highly efficient blocklist query scheme as a basic design, which conveniently achieves backward compatibility with current blockchain payment systems at a considerably low cost. Based on this design, we propose a new framework for shareholders to evaluate the quality of blocklists. Our framework provides stronger security guarantees than other similar works, as it is capable of suppressing both individual biasing and coercive manipulation at the same time. We provide a complete game-theoretic analysis and demonstrate comprehensive evaluation results to confirm the effectiveness and efficiency of our solutions, under the settings of a practical number of shareholders.

Recent works have empirically shown that neural network interpretability is susceptible to malicious manipulations. However, existing attacks against Interpretable Deep Learning Systems (IDLSes) all focus on the white-box setting, which is obviously unpractical in real-world scenarios. In this paper, we make the first attempt to attack IDLSes in the decision-based black-box setting. We propose a new framework called Dual Black-box Adversarial Attack (DBAA) which can generate adversarial examples that are misclassified as the target class, yet have very similar interpretations to their benign cases. We conduct comprehensive experiments on different combinations of classifiers and interpreters to illustrate the effectiveness of DBAA. Empirical results show that in all the cases, DBAA achieves high attack success rates and Intersection over Union (IoU) scores.

In Machine Learning, the emergence of the right to be forgotten gave birth to a paradigm named machine unlearning, which enables data holders to proactively erase their data from a trained model. Existing machine unlearning techniques focus on centralized training, where access to all holders’ training data is a must for the server to conduct the unlearning process. It remains largely underexplored about how to achieve unlearning when full access to all training data becomes unavailable. One noteworthy example is Federated Learning (FL), where each participating data holder trains locally, without sharing their training data to the central server. In this paper, we investigate the problem of machine unlearning in FL systems. We start with a formal definition of the unlearning problem in FL and propose a rapid retraining approach to fully erase data samples from a trained FL model. The resulting design allows data holders to jointly conduct the unlearning process efficiently while keeping their training data locally. Our formal convergence and complexity analysis demonstrate that our design can preserve model utility with high efficiency. Extensive evaluations on four real-world datasets illustrate the effectiveness and performance of our proposed realization.

As in-the-wild data are increasingly involved in the training stage, machine learning applications become more susceptible to data poisoning attacks. Such attacks typically lead to test-time accuracy degradation or controlled misprediction. In this paper, we investigate the third type of exploitation of data poisoning - increasing the risks of privacy leakage of benign training samples. To this end, we demonstrate a set of data poisoning attacks to amplify the membership exposure of the targeted class. We first propose a generic dirty-label attack for supervised classification algorithms. We then propose an optimization-based clean-label attack in the transfer learning scenario, whereby the poisoning samples are correctly labeled and look "natural" to evade human moderation. We extensively evaluate our attacks on computer vision benchmarks. Our results show that the proposed attacks can substantially increase the membership inference precision with minimum overall test-time model performance degradation. To mitigate the potential negative impacts of our attacks, we also investigate feasible countermeasures.

[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3111208]

The rapid growth of storage overhead on public blockchains has urged the use of light clients that only store a small fraction of blockchain data and rely on other bootstrapped full nodes for data retrievals. Unfortunately, current blockchain light client designs are far from satisfactory. First, outsourcing retrieval requests could raise severe concerns about result correctness and privacy threats. Second, current light clients do not support rich query and enforce fee payments to full nodes. Given that blockchain storage increases day by day, enabling effective rich blockchain queries and fairly compensating full nodes’ ever growing costs has become extremely necessary. In this article, we propose a general and secure paid query framework to simultaneously meet those demands above. Specifically, we leverage the integration of trusted hardware (e.g., Intel SGX) and smart contract as a starting point for building efficient yet secure query processing with fair payments. Then, we further craft several crucial performance and security refinement designs to boost query efficiency and enforce result correctness, and also explore an enclave-facilitated fair settlement mechanism for on-chain cost optimizations. We implement a prototype of our paid query framework and the experimental result has demonstrated its practically affordable cost.

As a promising alternative solution to cloud storage, decentralized storage networks (DSN) are widely anticipated to develop continuously and reshape the storage market share in the foreseeable future. In particular, one of the most important research problems is how to enforce the quality of service (QoS) in the context of storage solutions. Despite plenty of auditing-related works in the context of cloud storage, none of them can be directly applied to the decentralized storage paradigm. The challenges of designing a feasible storage auditing framework emanate from two aspects: 1) security problems unique to the decentralized settings and 2) performance overhead due to on-chain operations. In this article, we first put forward a basic storage auditing framework that satisfies the security and efficiency requirements, and outperforms the existing approaches. We also identify a critical and overlooked security problem that would compromise the integrity of storage auditing solutions in the blockchain paradigm. With our refined storage auditing design based on customized zero knowledge protocols, we propose a convenient mitigation solution in our revised security model. The evaluation results confirm that our solution would only incur a 10–15 percent increase in the overall auditing costs for common usage scenarios, compared to the basic design.

The explosive growth of the Internet of Things (IoT) is pushing forward the paradigm of fog computing services today. An important service for most fog-enhanced applications is geographic range-match, which means the fog-nodes can accurately collect sensed data from IoT devices based on their location distances. However, due to the increasing attacking surfaces, outsourcing range query operations to untrusted fog-nodes inevitably raises new privacy concerns about query content and device location exposure. In this article, we devise a new geographic range-match scheme for fog-enhanced services, which allows fog-nodes to securely collect range-based sensed data while protecting the location privacy of IoT devices. Our main idea is to formulate the problem of encrypted geographic queries as range-based pattern matching and carefully craft security schemes to enable efficient range queries in the ciphertext domain. The proposed range-match scheme is provably secure and can reduce accessible information during distance comparisons. We formally analyze the security strengths and complete the prototype implementation. The comprehensive experimental results demonstrate the practicality of our designs.

Browser extensions are widely used nowadays to customize users’ browsers with more functionalities, meanwhile introduce potential risks due to escalated privileges. Existing security mechanisms, such as Same Origin Policy and Content Security Policy, do not apply to browser extensions that can read and write on web applications at any time. In spite of the state-of-the-art industrial efforts that rely on centralized management to inspect and detect malicious behaviors massively, the detection-based method cannot analyze fast-evolving behaviors of malicious browser extensions. To this end, we adopt a novel approach to protect users from malicious browser extensions, where we consider the problem of malicious extensions on the side of web applications. From a high level point of view, web developers are allowed to specify sensitive parts in a web application by using our provided software enclave. With our proposed WebEnclave extension installed, when users visit a web application, sensitive information required for the web application to work normally is sealed into an isolated world locally that malicious extensions cannot access. Extensive evaluation of our built prototype shows it can effectively protect user secrets from malicious extensions with negligible performance overhead and usability inconvenience. We also publish source codes for public use.

ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this article, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design.

Outsourcing machine learning inference services to the cloud is getting increasingly popular. However, this also entails privacy risks to the provider's proprietary model and the client's sensitive data. Focusing on inference with decision trees, this article proposes a framework for securely and efficiently outsourcing decision tree inference. Targeting both privacy and efficiency, we propose a customized protocol using only lightweight cryptography in the online execution of secure inference. We resort to additive secret sharing and tackle the problems in various components including secure input feature selection, decision node evaluation, and inference result generation. Our protocol requires no interaction from the provider and client during online secure inference, a distinct advantage over prior works for practical deployment as they all operate under the client-provider setting where synchronous and continuous interaction is required. Performance evaluation demonstrates our security design's efficiency, as well as substantial performance benefits for the client (up to four orders of magnitude in computation and 163 times in communication), as opposed to prior art in the non-outsourcing setting. To facilitate the practical usage for meeting more service demands, we also investigate the extensions for secure outsourced inference of random forests and categorical feature-based decision trees.

Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, such as a list of friends and her profile. It is typical to collect these local views of social graphs and conduct graph learning tasks. However, learning over graphs can raise privacy concerns as these local views often contain sensitive information. In this paper, we seek to ensure private graph learning on a decentralized network graph. Towards this objective, we proposeSolitude, a new privacy-preserving learning framework based on graph neural networks (GNNs), with formal privacy guarantees based on edge local differential privacy. The crux ofSolitudeis a set of new delicate mechanisms that can calibrate the introduced noise in the decentralized graph collected from the users. The principle behind the calibration is the intrinsic properties shared by many real-world graphs, such as sparsity. Unlike existing work on locally private GNNs, our new framework can simultaneously protect node feature privacy and edge privacy, and can seamlessly incorporate with any GNN with privacy-utility guarantees. Extensive experiments on benchmarking datasets show thatSolitudecan retain the generalization capability of the learned GNN while preserving the users’ data privacy under given privacy budgets.

In recent years there has been growing popularity of leveraging cloud computing for storing and querying attributed graphs, which have been widely used to model complex structured data in various applications. Such trend of outsourced graph analytics, however, is accompanied with critical privacy concerns regarding the information-rich and proprietary attributed graph data. In light of this, we design, implement, and evaluate OblivGM, a new system aimed at oblivious graph analytics services outsourced to the cloud. OblivGM focuses on the support for attributed subgraph matching, one popular and fundamental graph query functionality aiming to retrieve from a large attributed graph subgraphs isomorphic to a small query graph. Built from a delicate synergy of insights from attributed graph modelling and advanced lightweight cryptography, OblivGM protects the confidentiality of data content associated with attributed graphs and queries, conceals the connections among vertices in attributed graphs, and hides search access patterns. Meanwhile, OblivGM flexibly supports oblivious evaluation of varying subgraph queries, which may contain equality and/or range predicates. Extensive experiments over a real-world attributed graph dataset demonstrate that while providing strong security guarantees, OblivGM achieves practically affordable performance (with query latency on the order of a few seconds).

Ethereum has been attracting lots of attacks, hence there is a pressing need to perform timely investigation and detect more attack instances. However, existing systems suffer from the scalability issue due to the following reasons. First, the tight coupling between malicious contract detection and blockchain data importing makes them infeasible to repeatedly detect different attacks. Second, the coarse-grained archive data makes them inefficient to replay transactions. Third, the separation between malicious contract detection and runtime state recovery consumes lots of storage. In this article, we propose a scalable attack detection framework named EthScope , which overcomes the scalability issue by neatly re-organizing the Ethereum state and efficiently locating suspicious transactions. It leverages the fine-grained state to support the replay of arbitrary transactions and proposes a well-designed schema to optimize the storage consumption. The performance evaluation shows that EthScope can solve the scalability issue, i.e., efficiently performing a large-scale analysis on billions of transactions, and a speedup of around \( \text{2,300}\times \) when replaying transactions. It also has lower storage consumption compared with existing systems. Further analysis shows that EthScope can help analysts understand attack behaviors and detect more attack instances.

Network function virtualization (NFV) enables elastic scaling to middlebox deployment and management. Therefore, efficient stateful scaling is an important task because operators often need to shift traffic and the associated flow states across VNF instances to deal with time-varying loads. Existing NFV scaling methods, however, typically focus on one aspect of the scaling pipeline and does not offer an end-to-end scaling framework. This article presents ScaleFlux, a complete stateful scaling system that efficiently reduces flow-level latency and achieves near-optimal resource usage. ScaleFlux (1) monitors traffic load for each VNF instance and adopts a queue-based mechanism to detect load burstiness timely, (2) deploys a flow bandwidth predictor to predict flow bandwidth time-series with the ABCNN-LSTM model, and (3) schedules the necessary flow and state migration using the simulated annealing algorithm to achieve both flow-level latency guarantee and resource usage minimization. Testbed evaluation with a five-machine cluster shows that ScaleFlux reduces flow completion time by at least 8.7× for all the workloads and achieves near-optimal CPU usage during scaling.

Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this article, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.

In cryptography and algorithmic number theory, modular inversion is viewed as one of the most common and time-consuming operations. It is hard to be directly accomplished on resource-constrained clients (e.g., mobile devices and IC cards) since modular inversion involves a great amount of operations on large numbers in practice. To address the above problem, this paper proposes a novel unimodular matrix transformation technique to realize secure outsourcing of modular inversion. This technique makes our algorithm achieve several amazing properties. First, to the best of our knowledge, it is the first secure outsourcing computation algorithm that supports arbitrary and variable modulus, which eliminates the restriction in previous work that the protected modulus has to be a fixed composite number. Second, our algorithm is based on the single untrusted program model, which avoids the non-collusion assumption between multiple servers. Third, for each given instance of modular inversion, it only needs one round interaction between the client and the cloud server, and enables the client to verify the correctness of the results returned from the cloud server with the (optimal) probability 1. Furthermore, we propose an extended secure outsourcing algorithm that can solve modular inversion in multi-variable case. Theoretical analysis and experimental results show that our proposed algorithms achieve remarkable local-client’s computational savings. At last, as two important and helpful applications of our algorithms, the outsourced implementations of the key generation of RSA algorithm and the Chinese Reminder Theorem are given.

Adversarial attacks against commercial black-box speech platforms, including cloud speech APIs and voice control devices, have received little attention until recent years. Constructing such attacks is difficult mainly due to the unique characteristics of time-domain speech signals and the much more complex architecture of acoustic systems. The current "black-box" attacks all heavily rely on the knowledge of prediction/confidence scores or other probability information to craft effective adversarial examples (AEs), which can be intuitively defended by service providers without returning these messages. In this paper, we take one more step forward and propose two novel adversarial attacks in more practical and rigorous scenarios. For commercial cloud speech APIs, we propose Occam, a decision-only black-box adversarial attack, where only final decisions are available to the adversary. In Occam, we formulate the decision-only AE generation as a discontinuous large-scale global optimization problem, and solve it by adaptively decomposing this complicated problem into a set of sub-problems and cooperatively optimizing each one. Our Occam is a one-size-fits-all approach, which achieves 100% success rates of attacks (SRoA) with an average SNR of 14.23dB, on a wide range of popular speech and speaker recognition APIs, including Google, Alibaba, Microsoft, Tencent, iFlytek, and Jingdong, outperforming the state-of-the-art black-box attacks. For commercial voice control devices, we propose NI-Occam, the first non-interactive physical adversarial attack, where the adversary does not need to query the oracle and has no access to its internal information and training data. We, for the first time, combine adversarial attacks with model inversion attacks, and thus generate the physically-effective audio AEs with high transferability without any interaction with target devices. Our experimental results show that NI-Occam can successfully fool Apple Siri, Microsoft Cortana, Google Assistant, iFlytek and Amazon Echo with an average SRoA of 52% and SNR of 9.65dB, shedding light on non-interactive physical attacks against voice control devices.

Nowadays, many data owners choose to outsource their data to public cloud servers while allowing authorized users to retrieve them. To protect data confidentiality from an untrusted cloud, many studies on searchable encryption (SE) are proposed for privacy-preserving search over encrypted data. However, most of the existing SE schemes only focus on the single-owner model. Users need to search one-by-one among data owners to retrieve relevant results even if data are from the same cloud server, which inevitably incurs unnecessary bandwidth and computation cost to users. Thus, how to enable efficient authorized search over multi-owner datasets remains to be fully explored. In this paper, we propose a new privacy-preserving search scheme for the multi-owner and multi-user model. Our proposed scheme has two main advantages: 1) We achieve an attribute-based keyword search for multi-owner model, where users can only search datasets from specific authorized owners. 2) Each data owner can enforce its own fine-grained access policy for users while an authorized user only needs to generate one trapdoor (i.e., encrypted search keyword) to search over multi-owner encrypted data. Through rigorous security analysis and performance evaluation, we demonstrate that our scheme is secure and feasible.

Blockchain scalability is one of the most desired properties for permissionless blockchain. Many recent blockchain protocols have focused on increasing the transaction throughput. However, existing protocols cannot dynamically scale the throughput to meet transaction demand. In this paper, we propose Occam, a secure and adaptive scaling scheme. Occam adaptively changes the transaction throughput by expanding and shrinking according to the transaction demand in the network. We introduce a dynamic adjustment mechanism of mining difficulty and a mining power load balancing mechanism to resist various attacks. Furthermore, we implement Occam on Amazon EC2 cluster with 1000 full nodes. Experimental results show that Occam can greatly increase the throughput of the blockchain and the mining power utilization.

Transfer learning eases the burden of training a well-performed model from scratch, especially when training data is scarce and computation power is limited. In deep learning, a typical strategy for transfer learning is to freeze the early layers of a pre-trained model and fine-tune the rest of its layers on the target domain. Previous work focuses on the accuracy of the transferred model but neglects the transfer of adversarial robustness. In this work, we first show that transfer learning improves the accuracy on the target domain but degrades the inherited robustness of the target model. To address such a problem, we propose a novel cooperative adversarially-robust transfer learning (CARTL) by pre-training the model via feature distance minimization and fine-tuning the pre-trained model with non-expansive fine-tuning for target domain tasks. Empirical results show that CARTL improves the inherited robustness by about 28% at most compared with the baseline with the same degree of accuracy. Furthermore, we study the relationship between the batch normalization (BN) layers and the robustness in the context of transfer learning, and we reveal that freezing BN layers can further boost the robustness transfer.

Data holders, such as mobile apps, hospitals and banks, are capable of training machine learning (ML) models and enjoy many intelligence services. To benefit more individuals lacking data and models, a convenient approach is needed which enables the trained models from various sources for prediction serving, but it has yet to truly take off considering three issues: (i) incentivizing prediction truthfulness; (ii) boosting prediction accuracy; (iii) protecting model privacy.We design FedServing, a federated prediction serving framework, achieving the three issues. First, we customize an incentive mechanism based on Bayesian game theory which ensures that joining providers at a Bayesian Nash Equilibrium will provide truthful (not meaningless) predictions. Second, working jointly with the incentive mechanism, we employ truth discovery algorithms to aggregate truthful but possibly inaccurate predictions for boosting prediction accuracy. Third, providers can locally deploy their models and their predictions are securely aggregated inside TEEs. Attractively, our design supports popular prediction formats, including top-1 label, ranked labels and posterior probability. Besides, blockchain is employed as a complementary component to enforce exchange fairness. By conducting extensive experiments, we validate the expected properties of our design. We also empirically demonstrate that FedServing reduces the risk of certain membership inference attack.

Motivated by resource-limited scenarios, knowledge distillation (KD) has received growing attention, effectively and quickly producing lightweight yet high-performance student models by transferring the dark knowledge from large teacher models. However, many pre-trained teacher models are downloaded from public platforms that lack necessary vetting, posing a possible threat to knowledge distillation tasks. Unfortunately, thus far, there has been little research to consider the backdoor attack from the teacher model into student models in KD, which may pose a severe threat to its wide use. In this paper, we, for the first time, propose a novel Anti-Distillation Backdoor Attack (ADBA), in which the backdoor embedded in the public teacher model can survive the knowledge distillation process and thus be transferred to secret distilled student models. We first introduce a shadow to imitate the distillation process and adopt an optimizable trigger to transfer information to help craft the desired teacher model. Our attack is powerful and effective, which achieves 95.92%, 94.79%, and 90.19% average success rates of attacks (SRoAs) against several different structure student models on MNIST, CIFAR-10, and GTSRB, respectively. Our ADBA also performs robustly under different user distillation environments with 91.72% and 92.37% average SRoAs on MNIST and CIFAR-10, respectively. Finally, we show that the ADBA has a low overhead in the injecting process, which converges on 50 and 70 epochs on CIFAR-10 and GTSRB, respectively, while the normal training epochs of these datasets are almost 200.

With the rapid development and widespread application of crowdsourcing, the limitations of traditional systems are gradually exposed. First, traditional systems fail to protect the privacy of task requesters and workers. They typically rely on a centralized server to aggregate the task content and workers' interests, while these data contain sensitive information. Second, crowdsourcing resources in each system are isolated. The tasks in one system cannot reach potential workers in other systems. Thus, there is a great need to build a new privacy-preserving and federated crowdsourcing system. However, the existing privacy-preserving solutions rely on a trusted third party to perform key management, which is not applicable in a federated setting. To this end, we propose the first proxy-free privacy-preserving and federated crowdsourcing system. It interconnects the existing crowdsourcing systems and can perform encrypted task matching across various systems without relying on a trusted third-party authority. Our main idea is to achieve federated crowdsourcing by moving secure task matching to the trusted smart contract. To get rid of the dependence on the trusted authority, we combine the rewritable deterministic hashing technique with searchable encryption schemes to achieve secure on-chain task-matching authorization. Moreover, we utilize the puncturable encryption technique to implement secure authorization revocation. We formally analyze the security of our design and implement a prototype on Ethereum. Evaluation results demonstrate that our design is secure and efficient for blockchain-based crowdsourcing.

Network function virtualization (NFV) has been promising to improve the availability, programmability, and flexibility of network function deployment and communication facilities. Meanwhile, with the advancements of cloud technologies, there has been a trend to outsource network functions through virtualization to a cloud service provider, so as to alleviate the local burdens on provisioning and managing such hardware resources. Promising as it is, redirecting the communication traffic to a third-party service provider has drawn various security and privacy concerns. Traditional end-to-end encryption can protect the traffic in transmit, but it also hinders data usability. This dilemma has raised wide interests from both industry and academia, and great efforts have been made to realize privacy-preserving network function outsourcing that can guarantee the confidentiality of network communications while preserving the ability to inspect the traffic. In this article, we conduct a comprehensive survey of the state-of-the-art literature on network function outsourcing, with a special focus on privacy and security issues. We first give a brief introduction to NFV and pinpoint its challenges and security risks in the cloud context. Then, we present detailed descriptions and comparisons of existing secure network function outsourcing schemes in terms of functionality, efficiency, and security. Finally, we conclude by discussing possible future research directions.

Blockchain has led the trend of decentralized applications and shown great use beyond cryptocurrencies. Decentralized storage such as Storj and Sia leverages blockchain to establish an open platform for sharing economy, which provides private and reliable file-outsourcing services. However, the ubiquitous keyword search function over encrypted files is yet to be supported. To enable this function, we first apply searchable encryption techniques to the decentralized setting. But this primitive can hardly ensure the service integrity. The reason is that decentralized storage commonly faces severe threats from both clients and service peers. Service peers may return partial or incorrect results, while clients may intentionally slander the service peers to avoid payments. To address these threats, we utilize the smart contract to record the logs of encrypted search (aka evidence) on the blockchain, and devise a fair protocol to handle disputes and issue fair payments. Using a dynamic-efficient searchable encryption scheme as an instantiation, we craft a concrete scheme that preserves encrypted search capability and enforces ecosystem healthiness, so that service peers are incentivized to make real efforts and jointly guarantee service reliability. We implement our scheme in Python and Solidity, and test its search performance and transaction costs on Ethereum.

Public blockchains have emerged as a promising direction in revolutionizing existing data-driven systems relying on centralized service providers. Among others, one kind of such systems is the popular crowdsensing systems which promise convenient data collection and aggregation. Although promising, leveraging public blockchains to build crowdsensing systems is non-trivial and has to overcome several barriers. First, public blockchains are transparent and lack support for data privacy. Second, participants from the open blockchain environment may misbehave in serving crowdsensing applications, like providing invalid data or doing aggregation incorrectly. Further, on-chain processing incurs monetary cost, so simply putting all workload on-chain is highly uneconomical and a delicate joint on-chain and off-chain design is required. In this paper, we take the first research attempt and explore a new design point to bridge public blockchains with crowdsensing systems. We propose a framework for building private, robust, and verifiable blockchain-empowered crowdsensing systems. It features an open service paradigm where blockchain nodes can rent out their computing resources to serve crowdsensing applications, with custom and full-fledged mechanisms to foster a healthy and economical ecosystem and to simultaneously tackle the challenges of data privacy, robustness against misbehaving participants, and service correctness assurance. Extensive experiments demonstrate our designs practicality.

It is increasingly popular to leverage the wisdom of crowd for knowledge discovery and monetization. Among others, crowdsensing with truth discovery has emerged as a promising way for leveraging the crowd wisdom, which can mine reliable knowledge from the generally unreliable sensory data contributed collected from diverse sources. Building a knowledge marketplace based on crowdsensing with truth discovery for knowledge discovery and monetization, however, is non-trivial and has to overcome several challenges. First, the sensory data should be protected as they may carry sensitive information. Second, many real crowdsensing applications usually yield sensory data in a streaming fashion, posing the demand that truth discovery should be conducted over data streams to continuously mine reliable knowledge in each data collection epoch. Third, knowledge monetization should be well treated, fully addressing the practical needs of parties in the monetization ecosystem. In this article, we take the first research attempt and propose a new full-fledged framework for building a secure knowledge marketplace over crowdsensed data streams. Our marketplace supports secure monetization of reliable knowledge mined privately from data streams in crowdsensing applications. Our framework leverages lightweight cryptographic techniques like additive secret sharing to enable privacy-preserving streaming truth discovery, continuously producing reliable knowledge over data streams. For monetization of the learned truth, i.e., knowledge, we resort to the emerging blockchain technology and deliver a tailored and full-fledged design, which promises monetization fairness, knowledge confidentiality, and streamlined processing. Extensive experiments on Amazon cloud and Ethereum blockchain demonstrate the practically affordable performance of our design.

Recently, deep neural networks have achieved state-of-the-art performance in multiple computer vision tasks, and become core parts of computer vision applications. In most of their implementations, a standard input preprocessing component called image scaling is embedded, in order to resize the original data to match the input size of pre-trained neural networks. This article demonstrates content disguising attacks by exploiting the image scaling procedure, which cause machine's extracted content to be dramatically dissimilar with that before scaled. Different from previous adversarial attacks, our attacks happen in the data preprocessing stage, and hence they are not subject to specific machine learning models. To achieve a better deceiving and disguising effect, we propose and implement three feasible attack approaches with L0- and L∞-norm distance metrics. We have conducted a comprehensive evaluation on various image classification applications, including three local demos and two remote proprietary services. We also investigate the attack effects on a YOLO-v3 object detection demo. Our experimental results demonstrate successful content disguising against all of them, which validate our approaches are practical.

Safe Browsing (SB) is an important security feature in modern web browsers to help detect new unsafe websites. Although useful, recent studies have pointed out that the widely adopted SB services, such as Google Safe Browsing and Microsoft SmartScreen, can raise privacy concerns since users' browsing history might be subject to unauthorized leakage to service providers. In this paper, we present a Privacy-Preserving Safe Browsing (PPSB) platform. It bridges the browser that uses the service and the third-party blacklist providers who provide unsafe URLs, with the guaranteed privacy of users and blacklist providers. Particularly, in PPSB, the actual URL to be checked, as well as its associated hashes or hash prefixes, never leave the browser in cleartext. This protects the user's browsing history from being directly leaked or indirectly inferred. Moreover, these lists of unsafe URLs, the most valuable asset for the blacklist providers, are always encrypted and kept private within our platform. Extensive evaluations using real datasets (with over 1 million unsafe URLs) demonstrate that our prototype can function as intended without sacrificing normal user experience, and block unsafe URLs at the millisecond level. All resources, including Chrome extension, Docker image, and source code, are available for public use.

Searchable symmetric encryption (SSE) allows the data owner to outsource an encrypted database to a remote server in a private manner while maintaining the ability for selectively search. So far, most existing solutions focus on an honest-but-curious server, while security designs against a malicious server have not drawn enough attention. A few recent works have attempted to construct verifiable SSE that enables the data owner to verify the integrity of search results. Nevertheless, these verification mechanisms are highly dependent on specific SSE schemes, and fail to support complex queries. A general verification mechanism is desired that can be applied to all SSE schemes. In this work, instead of concentrating on a central server, we explore the potential of the smart contract, an emerging blockchain-based decentralized technology, and construct decentralized SSE schemes where the data owner can receive correct search results with assurance without worrying about potential wrongdoings of a malicious server. We study both public and private blockchain environments and propose two designs with a trade-off between security and efficiency. To better support practical applications, the multi-user setting of SSE is further investigated where the data owner allows authenticated users to search keywords in shared documents. We implement prototypes of our two designs and present experiments and evaluations to demonstrate the practicability of our decentralized SSE schemes.

Cross-media retrieval (CMR) is an attractive networked application where a server responds to queries with retrieval results of different modalities. Different from traditional information retrieval, CMR relies on a more enriched set of machine learning techniques to produce semantic models projecting multimodal data into a common space. A larger training dataset usually gives more accurate models, leading to a better retrieval result. Despite very promising with potential underpinnings in network analytics and multimedia applications, applying CMR in such contexts also faces severe privacy challenges, due to the fact that various data scattering among multiple parties may be sensitive and not allowed to be shared publicly. Studies jointly considering cross-media analytics, privacy protection, collaborative learning, and distributed networking contexts, are relatively sparse. In this work, we propose the first practical system for privacy-preserving cross-media retrieval by utilizing trusted processors. Our scheme enables secure aggregation of the data from distinct parties, and secure canonical correlation analysis (CCA) over collaborated data to obtain semantic models. Verification mechanisms are designed to defend against active attacks from a malicious adversary. Furthermore, to deal with large data sets, we provide a set of optimization methods to accomodate to limited trusted memory and improve the efficiency of training process in CMR. We consider issues such as data block splitting to manage memory overhead, ordering of operations as well as parameters reuse and release to simplify I/O, and parallel computation to speed up dual operations. Our experiments over both synthetic and real datasets show that our solution is very efficient in practice, outperforms the existing solutions, and performs comparably with the original CMR system.

Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including Server-Local Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the whole system. The proposed scheme is provably secure in the random oracle model under the Decisional q-Combined Bilinear Diffie-Hellman (q-DCDBH) assumption. Comprehensive theoretical analysis and detailed simulation results show that the proposed scheme outperforms the state-of-the-art work.

Large volumes of images are being exponentially generated today, which poses high demands on the services of storage, processing, and management. To handle the explosive image growth, a natural choice nowadays is cloud computing. However, coming with the cloud-based image services is acute data privacy concerns, which has to be well addressed. In this paper, we present a secure cloud-based image service framework, which allows privacy-preserving and effective image denoising on the cloud side to produce high-quality image content, a key for assuring the quality of various image-centric applications. We resort to state-of-the-art image denoising techniques based on deep neural networks (DNNs), and show how to uniquely bridge cryptographic techniques (like lightweight secret sharing and garbled circuits) and image denoising in depth to support privacy-preserving DNN based image denoising services on the cloud. By design, the image content and the DNN model are all kept private along the whole cloud-based service flow. Our extensive empirical evaluation shows that our security design is able to achieve denoising quality comparable to that in plaintext, with high cost efficiency on the local side and practically affordable cost on the cloud side.

Searchable symmetric encryption (SSE) enables users to search over encrypted documents in untrusted clouds without leaking the search keywords to the clouds. Existing SSE schemes achieve high search efficiency at the expense of leaking access patterns and search patterns, where clouds can recover a large percentage of queried keywords using the leaked access patterns and search patterns. To prevent clouds from recovering users' keywords, researchers have proposed a number of solutions to protect either search patterns or access patterns. However, none of them can protect both access patterns and search patterns. Moreover, existing SSE schemes cannot work in the generic database setting that allows multiple users to write or read over encrypted documents. In this paper, we propose an efficient searchable symmetric encryption scheme, called SAP-SSE, which protects both access patterns and search patterns in the generic database setting. The main idea of protecting search patterns is to leverage re-encryption cryptosystems to shuffle index entries over multiple clouds. To protect access patterns, we distribute secure indexes to multiple clouds and then propose an index redistribution protocol that allows users to renew index entries in clouds. Furthermore, SAP-SSE provides a configurable security policy to balance security and efficiency. Formal security analysis and experimental evaluation show that SAP-SSE can prevent pattern leakage with low overhead.

Searchable symmetric encryption (SSE) enables users to make confidential queries over always encrypted data while confining information disclosure to pre-defined leakage profiles. Despite the well-understood performance and potentially broad applications of SSE, recent leakage-abuse attacks (LAAs) are questioning its real-world security implications. They show that a passive adversary with certain prior information of a database can recover queries by exploiting the legitimately admitted leakage. While several countermeasures have been proposed, they are insufficient for either security, i.e., handling only specific leakage like query volume, or efficiency, i.e., incurring large storage and bandwidth overhead. We aim to fill this gap by advancing the understanding of LAAs from a fundamental algebraic perspective. Our investigation starts by revealing that the index matrices of a plaintext database and its encrypted image can be linked by linear transformation. The invariant characteristics preserved under the transformation encompass and surpass the information exploited by previous LAAs. They allow one to unambiguously link encrypted queries with corresponding keywords, even with only partial knowledge of the database. Accordingly, we devise a new powerful attack and conduct a series of experiments to show its effectiveness. In response, we propose a new security notion to thwart LAAs in general, inspired by the principle of local differential privacy (LDP). Under the notion, we further develop a practical countermeasure with tunable privacy and efficiency guarantee. Experiment results on representative real-world datasets show that our countermeasure can reduce the query recovery rate of LAAs, including our own.

Proofs of retrievability and proofs of replication are two cryptographic tools that enable a remote server to prove that the users' data has been correctly stored. Nevertheless, the literature either requires the users themselves to perform expensive verification jobs, or relies on a “fully trustworthy” third party auditor (TPA) to execute the public verification. In addition, none of existing solutions consider the underlying incentive issues behind a rational server who is motivated to collect users' data but tries to evade the replication checking in order to save storage resources. In this article, we propose the first decentralized system for proofs of data retrievability and replication-BOSSA, which is incentive-compatible for each party and realizes automated auditing atop off-the-shelf blockchain platforms. We deal with issues such as proof enforcements to catch malicious behaviors, new metrics to measure the contributions, and reward distributions to create a fair reciprocal environment. BOSSA also incorporates privacy-enhancing techniques to prevent decentralized peers (including blockchain nodes) from inferring private information about the outsourced data. Security analysis is presented in the context of integrity, privacy, and reliability. We implement a prototype based on BOSSA leveraging the smart contracts of Ethereum blockchain. Our extensive experimental evaluations demonstrate the practicality of our proposal.

Privacy-preserving similarity search plays an essential role in data analytics, especially when very large encrypted datasets are stored in the cloud. Existing mechanisms on privacy-preserving similarity search were not able to support secure updates (addition and deletion) efficiently when frequent updates are needed. In this article, we propose a new mechanism to support parallel privacypreserving similarity search in a distributed key-value store in the cloud, with a focus on efficient addition and deletion operations, both executed with sublinear time complexity. If search accuracy is the top priority, we further leverage Yao's garbled circuits and the homomorphic property of Hash-ElGamal encryption to build a secure evaluation protocol, which can obtain the top-R most accurate results without extensive client-side post-processing. We have formally analyzed the security strength of our proposed approach, and performed an extensive array of experiments to show its superior performance as compared to existing mechanisms in the literature. In particular, we evaluate the performance of our proposed protocol with respect to the time it takes to build the index and perform similarity queries. Extensive experimental results demonstrated that our protocol can speedup the index building process by up to 800x with 2 threads and the similarity queries by up to -7x with comparable accuracy, as compared to the state-of-the-art in the literature.

Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solutions still lack strong assurances on: 1) service correctness (i.e., whether the MLaaS works as expected); 2) trustworthy accounting (i.e., whether the bill for the MLaaS resource consumption is correctly accounted); 3) fair payment (i.e., whether a client gets the entire MLaaS result before making the payment). Without these assurances, unfaithful service providers can return improperly-executed ML task results or partially-trained ML models while asking for over-claimed rewards. Moreover, it is hard to argue for wide adoption of MLaaS to both the client and the service provider, especially in the open market without a trusted third party. In this article, we present VeriML, a novel and efficient framework to bring integrity assurances and fair payments to MLaaS. With VeriML, clients can be assured that ML tasks are correctly executed on an untrusted server, and the resource consumption claimed by the service provider equals to the actual workload. We strategically use succinct non-interactive arguments of knowledge (SNARK) on randomly-selected iterations during the ML training phase for efficiency with tunable probabilistic assurance. We also develop multiple ML-specific optimizations to the arithmetic circuit required by SNARK. Our system implements six common algorithms: linear regression, logistic regression, neural network, support vector machine, K-means and decision tree. The experimental results have validated the practical performance of VeriML.

In-network storage is recognized as a vital component of many emerging network architectures, which facilitates high-quality and efficient content-centric services. In this trend, providing content-based near-duplicate detection (NDD) services among in-network storage becomes naturally necessary for network traffic alleviation and resource optimization. However, due to the increasing attacking surfaces, storing data in the networked environment inevitably raises new concerns about user privacy exposure and unauthorized data access. Therefore, we aim to design a secure NDD service in the context of encrypted in-network storage. For efficiency, we first leverage the fingerprint techniques and locality-sensitive hashing to convert the problem of NDD into the keyword search. We then adopt an efficient multi-key searchable encryption scheme, which requires only one encrypted query from the user even the data are from multiple content providers encrypted with different keys. As simply combining the above methods does not appear to directly locate accurate results, we then devise a secure result refining scheme via Yao's garbled circuits to avoid user-side post-processing. Furthermore, we enhance our design to address the potential malicious behavior of in-network servers. Extensive evaluations of real-world image dataset demonstrate that our design can achieve comparable accuracy to the plaintext with modest security overhead.

An encrypted index is a data structure that assisting untrusted servers to provide various query functionalities in the ciphertext domain. Although traditional index designs can prevent servers from directly obtaining plaintexts, the confidentiality of outsourced data could still be compromised by observing the volume of different queries. Recent volume attacks have demonstrated the importance of sealing volume-pattern leakage. To this end, several works are made to design secure indexes with the volume-hiding property. However, prior designs only work for encrypted keyword search. Due to the unpredictable range query results, it is difficult to protect the volume-pattern leakage while achieving efficient range queries.In this paper, for the first time, we define and solve the challenging problem of volume-hiding range queries over encrypted data. Our proposed hybrid index framework, called HybrIDX, allows an untrusted server to efficiently search encrypted data based on order conditions without revealing the exact volume size. It resorts to the trusted hardware techniques to assist range query processing by moving the comparison algorithm to trusted SGX enclaves. To enable volume-hiding data retrieval, we propose to host encrypted results outside the enclave in an encrypted multimaps manner. Apart from this novel hybrid index design, we further customize a bulk refresh mechanism to enable accesspattern obfuscation. We formally analyze the security strengths and complete the prototype implementation. Evaluation results demonstrate the feasibility and practicability of our designs.

How to audit outsourced data in centralized storage like cloud is well-studied, but it is largely under-explored for the rising decentralized storage network (DSN) that bodes well for a billion-dollar market. To realize DSN as a usable service in a fully decentralized manner, the blockchain comes in handy - to record and verify audit trails in forms of proof of storage, and based on that, to enforce fair payments with necessary dispute resolution. Leaving the audit trails on the blockchain offers transparency and fairness, yet it 1) sacrifices privacy, as they may leak information about the data under audit, and 2) overwhelms onchain resources, as they may be practically large in size and expensive to verify. Prior auditing designs in centralized settings are not directly applicable here. A handful of proposals targeting DSN cannot satisfactorily address these issues either. We present an auditing solution that addresses on-chain privacy and efficiency, from a synergy of homomorphic linear authenticators with polynomial commitments for succinct proofs, and the sigma protocol for provable privacy. The solution results in, per audit, 288-byte proof written to the blockchain, and constant verification cost. It can sustain long-term operation and easily scale to thousands of users on Ethereum.

Current blockchain systems suffer from a number of inherent drawbacks in its scalability, latency, and processing throughput. By enabling parallel confirmations of transactions, sharding has been proposed to mitigate these drawbacks, which usually requires frequent communication among miners through a separate consensus protocol.In this paper, we propose, analyze, and implement a new distributed and dynamic sharding system to substantially improve the throughput of blockchain systems based on smart contracts, while requiring minimum cross-shard communication. Our key observation is that transactions sent by users who only participate in a single smart contract can be validated and confirmed independently without causing double spending. Therefore, the natural formation of a shard is to surround one smart contract to start with. The complication lies in the different sizes of shards being formed, in which a small shard with few transactions tends to generate a large number of empty blocks resulting in a waste of mining power, while a large shard adversely affects parallel confirmations. To overcome this problem, we propose an inter-shard merging algorithm with incentives to encourage small shards to merge with one another and form a larger shard, an intra-shard transaction selection mechanism to encourage miners to select different subsets of transactions for validation, as well as a parameter unification method to further improve these two algorithms to reduce the communication cost and improve system reliability.We analyze our proposed algorithms using the game theoretic approach, and prove that they converge to a Nash Equilibrium. We also present a security analysis on our sharding design, and prove that it resists adversaries who occupy at most 33% of the computation power. We have implemented our designs on go-Ethereum 1.8.0 and evaluated their performance using both real-world blockchain transactions and large-scale simulations. Our results show that throughput has been improved by 7.2×, and the number of empty blocks has been reduced by 90%.

The rapid growth of Ethereum blockchain has brought extremely heavy overhead for coin owners or developers to bootstrap and access transactions on Ethereum. To address this, light client is enabled, which only stores a small fraction of blockchain data and relies on bootstrapped full nodes for transaction retrievals. However, because the retrieval requests are outsourced, it raises several severe concerns about the integrity of returned results and the leakage of sensitive blockchain access histories, largely hindering the wider adoption of this important lightweight design. In addition to security issues, the continuously increasing blockchain storage also urges for more effective query functionalities for the Ethereum blockchain, so as to enable more flexible and precise transaction retrievals.In this paper, we propose EncELC, a new Ethereum light client design that enforces full-fledged protections for clients and enables rich queries over the Ethereum blockchain. EncELC leverages trusted hardware (e.g., Intel SGX) as a starting point for building efficient yet secure processing, and further crafts several crucial performance and security refinement designs to boost query efficiency and conceal leakages inside and outside SGX enclave. We implement a prototype of EncELC and test its performance in several real settings, and the results have confirmed the practicality of EncELC.

Federated learning (FL), as a privacy-preserving machine learning framework, draws growing attention in both industry and academia. It obtains a jointly accurate model by distributing training tasks into data owners and aggregating their model updates. However, FL faces new security problems, as it losses direct control to training processes. One fundamental demand is to ensure whether participants execute training tasks as intended.In this paper, we propose TrustFL, a practical scheme that leverages Trusted Execution Environments (TEEs) to build assurance of participants' training executions with high confidence. Specifically, we use TEE to randomly check a small fraction of all training processes for tunable levels of assurance, while all computations are executed on the co-located faster yet insecure processor (e.g., GPU) for efficiency. To prevent various cheating behaviors like only processing TEE-requested computations or uploading old results, we devise a commitment-based method with specific data selection. We prototype TrustFL using GPU and SGX and evaluate its performance. The results show that TrustFL achieves one/two orders of magnitude speedups compared with naive training with SGX, when assuring correct training with a confidence level of 99%.

The explosive growth of network traffic is pushing forward the paradigm of cloud-based middlebox services today. However, due to the increasing attacking surfaces, redirecting enterprises traffic to outsourced middleboxes inevitably raises new privacy concerns about packet content exposure and unauthorized rulesets access. To address these issues, recent efforts have been made toward enabling middlebox services through encrypted traffic and middlebox rules. Following this direction, in this article, we investigate the issue of privacy-preserving header checking, which is an indispensable service of middlebox applications. Specifically, we propose two new encrypted header-matching schemes that significantly improve security and efficiency. Our main idea is to formulate the problem of encrypted header checking as range-based pattern matching, and carefully craft security designs to enable efficient header inspection in the ciphertext domain. Our first design is carefully tailored to generic range-based functions, while our second design is highly customized for contiguous rulesets to further improve the checking efficiency. We formally analyze the security strengths and implement a fully functional system prototype. The extensive experiments over the real-world rulesets demonstrate the practicality of our designs.

Autonomous vehicles (AVs) have promised to drastically improve the convenience of driving by releasing the burden of drivers and reducing traffic accidents with more precise control. With the fast development of artificial intelligence and significant advancements of the Internet of Things technologies, we have witnessed the steady progress of autonomous driving over the recent years. As promising as it is, the march of autonomous driving technologies also faces new challenges, among which security is the top concern. In this article, we give a systematic study on the security threats surrounding autonomous driving, from the angles of perception, navigation, and control. In addition to the in-depth overview of these threats, we also summarize the corresponding defense strategies. Furthermore, we discuss future research directions about the new security threats, especially those related to deep-learning-based self-driving vehicles. By providing the security guidelines at this early stage, we aim to promote new techniques and designs related to AVs from both academia and industry and boost the development of secure autonomous driving.

Cloud storage auditing schemes for shared data refer to checking the integrity of cloud data shared by a group of users. User revocation is commonly supported in such schemes, as users may be subject to group membership changes for various reasons. Previously, the computational overhead for user revocation in such schemes is linear with the total number of file blocks possessed by a revoked user. The overhead, however, may become a heavy burden because of the sheer amount of the shared cloud data. Thus, how to reduce the computational overhead caused by user revocations becomes a key research challenge for achieving practical cloud data auditing. In this paper, we propose a novel storage auditing scheme that achieves highly-efficient user revocation independent of the total number of file blocks possessed by the revoked user in the cloud. This is achieved by exploring a novel strategy for key generation and a new private key update technique. Using this strategy and the technique, we realize user revocation by just updating the non-revoked group users' private keys rather than authenticators of the revoked user. The integrity auditing of the revoked user's data can still be correctly performed when the authenticators are not updated. Meanwhile, the proposed scheme is based on identity-base cryptography, which eliminates the complicated certificate management in traditional Public Key Infrastructure (PKI) systems. The security and efficiency of the proposed scheme are validated via both analysis and experimental results.

The wide adoption of cloud greatly facilitates the sharing of explosively generated media content today, yet deprives content providers' direct control over the outsourced media content. Thus, it is pivotal to build an encrypted cloud media center where only authorized access is allowed. Enforcing access control alone, however, cannot fully protect content providers' interests, as authorized users may later become traitors that illegally redistribute media content to the public. Such realistic threat should have been seriously treated yet is largely overlooked in the literature. In this paper, we initiate the first study on secure media sharing with fair traitor tracing in the encrypted cloud media center, through a new marriage of proxy re-encryption (for secure media sharing) and fair watermarking (for fair traitor tracing). Our key insight is to fully leverage the homomorphic properties residing in proxy re-encryption to embrace operations in fair watermarking. Two protocols are proposed for different application scenarios. We also provide complexity analysis for performance, showing that our work can also be treated as secure outsourcing of fair watermarking, and thus kills two birds with one stone. We thoroughly analyze the security strengths and conduct extensive experiments to validate the effectiveness of our design.

Truth discovery in mobile crowdsensing has recently received wide attention. It refers to the procedure for estimating the unknown user reliability from collected sensory data and inferring truthful information via reliability-aware data aggregation. Though widely studied in the plaintext domain, truth discovery remains largely under-explored in privacy-aware mobile crowdsensing. Existing works either do not consider user reliability issue or fall short of achieving practical cost efficiency, due to iterative transmission and computation over large ciphertexts from homomorphic cryptosystem. In this paper, we propose two new privacy-aware crowdsensing designs with truth discovery that significantly improve the bandwidth and computation performance on individual users. Our insight is to identify the core atomic operation in the iterative truth discovery procedure, and carefully craft security designs accordingly to enable efficient truth discovery in the ciphertext domain. Our first design is highly customized for the single-server setting, while our second design under the two-server model further shifts most of user workloads to the cloud server side. Both our designs protect individual sensory data and reliability degrees throughout the truth discovery procedure. Experiments show that compared with the prior result, our designs gain at least 30x and 10x savings on user communication and computation, respectively.

Finding min-cut is a fundamental operation in graph theory. It has been widely used in many applications such as image segmentation and network partition. However, solving the min-cut problem is time-consuming for resource-constrained devices, especially when the graph is large and dense. In this paper, we explore how to securely solve the min-cut problem in the cloud environment, and propose two secure outsourcing schemes for the min-cut of undirected edge-weighted graphs. The first scheme is based on two non-colluded untrusted cloud servers, and the second one is under the single untrusted cloud server model. In the designed schemes, we develop a new technique that contains merging vertices and edges, inserting fake vertices and edges, shuffling vertices and randomizing weights of edges to protect the privacy of graphs. In order to realize the checkability of result from a single untrusted cloud server, we design a novel verification mechanism by outsourcing the min-cut of two related graphs. Besides, we also provide security analysis and the experimental evaluation. To the best of our knowledge, it is the first research on secure outsourcing of graph algorithm.

The persistent growth of big data applications has being raising new challenges in managing large volumes of datasets with high scalability, confidentiality protection, and flexible types of search queries. In this paper, we propose a secure design to disassemble the private dataset with the aim to store them across geographically distributed servers while supporting secure multi-client Boolean queries. In this design, the data owner encrypts the private database with the searchable index attributes. The encrypted dataset will be disassembled and distributed evenly across multiple servers by leveraging the property of a distributed index framework. By constructing an encryption structure, generating search tokens, and enabling parallel query, we show how the proposed design performs the secure while efficient Boolean search. These queries are not only limited to those initiated by the data owner but also can be extended to support multiple authorized clients, where each client is allowed to access a necessary part of the private database. In this stage, we advocate a non-interactive authorization scheme where data owner is not required to stay online to process the query request. Moreover, the query operation can be executed in parallel, which significantly improves the search efficiency. We formally characterize the leakage profile, which allow us to follow the existing security analysis method to demonstrate that our system can guarantee data confidentiality and query privacy. To validate our protocol, we implement a system prototype and evaluate the efficiency of our construction. Through experimental results, we demonstrate the effectiveness of our protocol in terms of data outsourcing time and Boolean query time.

With the prosperity of mobile application markets, mobile advertising is becoming an increasingly important economic force. In order to maximize revenue, ads are recommended to be delivered to potentially interested users, which requires user targeting, i.e., analyzing users' profiles and exploring users' interests. However, collecting user personal information for targeted mobile advertising services raises critical privacy concerns. Although some solutions like anonymization and obfuscation have been proposed for privacy-aware targeted advertising, they undesirably face the issues of security, efficiency, and/or ad relevance. In this paper, we propose a practical system enabling secure and efficient targeted mobile advertising services. It allows the ad network to perform accurate user targeting, while ensuring strong privacy protection for mobile users. Specifically, we show how to properly leverage a cryptographic primitive called private stream searching to support secure, accurate, and practical targeted mobile ad delivery. Moreover, we propose secure billing schemes to enable the ad network to charge advertisers in a privacy-preserving manner. The security strength of our system is thoroughly analyzed. Through extensive experiments, we show that our system achieves practical efficiency on mobile devices.

Running off-site software middleboxes at third-party service providers has been a popular practice. However, routing large volumes of raw traffic, which may carry sensitive information, to a remote site for processing raises severe security concerns. Prior solutions often abstract away important factors pertinent to real-world deployment. In particular, they overlook the significance of metadata protection and stateful processing. Unprotected traffic metadata like low-level headers, size and count, can be exploited to learn supposedly encrypted application contents. Meanwhile, tracking the states of 100,000s of flows concurrently is often indispensable in production-level middleboxes deployed at real networks.

Multi-Writer Searchable Encryption, also known as public-key encryption with keyword search(PEKS), serves a wide spectrum of data sharing applications. It allows users to search over encrypted data encrypted via different keys. However, most of the existing PEKS schemes are built on classic security assumptions, which are proven to be untenable to overcome the threats of quantum computers. To address the above problem, in this paper, we propose a lattice-based searchable encryption scheme from the learning with errors (LWE) hardness assumption. Specifically, we observe that the keys of each user in a basic scheme are composed of large-sized matrices and basis of the lattice. To reduce the complexity of key management, our scheme is designed to enable users to directly use their identity for data encryption. We present several optimization techniques for implementation to make our design nearly practical. For completeness, we conduct rigorous security, complexity, and parameter analysis on our scheme, and perform comprehensive evaluations at a commodity machine. With a scenario of 100 users, the cost of key generation for each user is 125s, and the cost of searching a document with 1000 keywords is 13.4ms.

The emerging hardware-assisted security technologies facilitate the deployment of secure and trustworthy applications in today's cloud computing infrastructure. Despite promising, the advantages appear to diminish due to limited resources of trusted execution environments and ever-increasing workload to be processed inside. Different from existing task-specific and system-level optimizations, our key observation is that those redundant computations occur commonly among several applications when handling the same input data. In light of this, we propose SPEED, a secure and generic computation deduplication system in the context of Intel SGX. It allows SGX-enabled applications to identify redundant computations and reuse computation results, while protecting the confidentiality and integrity of code, inputs, and results. To maximize the benefit of computation deduplication, we design a cross-application deduplication scheme, empowering multiple applications to securely utilize the shared results as long as they perform identical computations. To ease the use of SPEED, we implement a fully functional prototype and provide a concise and expressive API for developers to deduplicate rich computations with minimal effort, as few as 2 lines of code per function call. Extensive evaluations of four popular applications demonstrate that SPEED improves performance by up to 400 times. The source code is available on GitHub for public use.

Data outsourcing to cloud has been a common IT practice nowadays due to its significant benefits. Meanwhile, security and privacy concerns are critical obstacles to hinder the further adoption of cloud. Although data encryption can mitigate the problem, it reduces the functionality of query processing, e.g., disabling SQL queries. Several schemes have been proposed to enable one-dimensional query on encrypted data, but multi-dimensional range query has not been well addressed. In this paper, we propose a secure and scalable scheme that can support multi-dimensional range queries over encrypted data. The proposed scheme has three salient features: (1) Privacy: the server cannot learn the contents of queries and data records during query processing. (2) Efficiency: we utilize hierarchical cubes to encode multi-dimensional data records and construct a secure tree index on top of such encoding to achieve sublinear query time. (3) Verifiability: our scheme allows users to verify the correctness and completeness of the query results to address server's malicious behaviors. We perform formal security analysis and comprehensive experimental evaluations. The results on real datasets demonstrate that our scheme achieves practical performance while guaranteeing data privacy and result integrity.

Voice biometrics is widely adopted for identity authentication in mobile devices. However, voice authentication is vulnerable to spoofing attacks, where an adversary may deceive the voice authentication system with pre-recorded or synthesized samples from the legitimate user or by impersonating the speaking style of the targeted user. In this paper, we design and implement VoicePop, a robust software-only anti-spoofing system on smartphones. VoicePop leverages the pop noise, which is produced by the user breathing while speaking close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to record by replay attacks beyond a certain distance and to imitate precisely by impersonators. We design a novel pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish individually unique relationship between phonemes and pop noises to identify legitimate users and defend against spoofing attacks. Our experimental results with 18 participants and three types of smartphones show that VoicePop achieves over 93.5% detection accuracy at around 5.4% equal error rate. VoicePop requires no additional hardware but only the built-in microphones in virtually all smartphones, which can be readily integrated in existing voice authentication systems for mobile devices.

Searchable encryption (SE) is a practical crypto-graphic primitive to build encrypted databases. Recently there has been much attention in leakage-abuse attacks against SE. Among others, attacks based on inference of keyword frequency can easily identify query keywords from the access pattern, i.e., query results. To mitigate these attacks, database padding is considered as a conceptually simple yet effective counter-measure. Unfortunately, none of the existing studies formally understand the relationship between padding security strength and its overhead. Also, how to craft padding is not restricted in current countermeasures, where bogus files are likely to be distinguishable from real ones. In this paper, we propose an information theory based framework to analyse the security strength under certain padding overhead. First, we leverage relative entropy to measure the “closeness” between the distributions of the original dataset and padded dataset. Second, we quantity the attack efforts against padding countermeasures by entropy analysis. Apart from theoretical findings, we further devise an algorithm via outlier detection for padding generation, which considers both the padded dataset distribution and the similarity between real and bogus files. Evaluations on a real-world dataset confirm our theoretical results and demonstrate the efficiency and effectiveness of our proposed padding generation algorithm.

In-the-cloud middleboxes have drawn widespread attentions recently, along with the rapid advancement of network function virtualization (NFV). Despite the well known benefits like reduced hardware and maintenance cost, deploying middleboxes in the remote environment poses new performance and security concerns, due to invisibility of the untrusted cloud and susceptible software implementations. One essential requirement for enterprise customers is to monitor performance compliance, while ensuring that packets are faithfully processed by remote middleboxes. In this paper, we propose a practical scheme towards verifiable performance measurement over in-the-cloud middleboxes. It employs “sample and replay” to achieve performance measurement and packet processing attestation. It estimates performance by collecting receipts in a tunable way, while coping with dynamic traffic changes made by middleboxes. In particular, our sampling is stateful which can capture a sequence of packets sharing same states of middleboxes for correct local replay. More importantly, it ensures high-confidence packet processing attestation by enforcing middleboxes to bind execution assurances with packets using commitment messages, and by using delayed verification procedure to defeat any potential biased results against selected sampling. To demonstrate the feasibility and efficiency of our scheme, we implement a prototype consisting of various types of middleboxes on Click, and conduct extensive experiments on Amazon EC2 with real traces. The experimental results show that our scheme imposes marginal processing delay for packets with various middleboxes and presents negligible throughput degradation.

Promising unprecedented convenience, Online Ride Hailing (ORH) service such as Uber and Didi has gained increasing popularity. Different from traditional taxi service, this new on-demand transportation service allows users to request rides from the online service providers at the touch of their fingers. Despite such great convenience, existing ORH systems require the users to expose their locations when requesting rides - a severe privacy issue in the face of untrusted or compromised service providers. In this paper, we propose a private yet efficient ride request scheme, allowing the user to enjoy public ORH service without sacrificing privacy. Unlike previous works, we consider a more practical setting where the information about the drivers and road networks is public. This poses an open challenge to achieve strong security and high efficiency for the secure ORH service. Our main leverage in addressing this problem is hardware-enforced Trusted Execution Environment, in particular Intel SGX enclave. However, the use of secure enclave does not lead to an immediate solution due to the hardware's inherent resource constraint and security limitation. To tackle the limited enclave space, we first design an efficient ride-matching algorithm utilizing hub-based labeling technique, which avoids loading massive road network data into enclave during online processing. To defend against side-channel attacks, we take the next step to make the ride-matching algorithm data-oblivious, by augmenting it with oblivious label access and oblivious distance computation. The proposed solution provides high efficiency of real-time response and strong security guarantee of data-obliviousness. We implement a prototype system of the proposed scheme and thoroughly evaluate it from both theoretical and experimental aspects. The results show that the proposed scheme permits accurate and real-time ride-matching with provable security.

Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.

Fog computing has emerged as a promising paradigm in overcoming the growing challenges (e.g., low latency, location awareness, and geographic distribution) arising from many real-world Internet of Things (IoT) applications, by extending the cloud to the network edge. With the widespread deployment of fog-assisted IoT applications, unprecedentedly huge volumes of network traffic from massive IoT devices would continuously arrive at the fog nodes. Archiving the network traffic can be highly beneficial to fog computing, which forms the basis of forensic, monitoring, troubleshooting, and many other critical tasks. Such high value, however, constantly renders traffic archives the first-order target to experienced attackers. This mandates the traffic archives to be built in a trustworthy way and stayed encrypted at rest. Security aside, it is yet highly desirable to retain the utility of the encrypted traffic archives, in particular by making them privately queryable. In this paper, we take the first research attempt and explore a new design point to delicately bridge trusted hardware and searchable encryption for building trustworthy, encrypted, yet queryable network traffic archives for fog-assisted IoT applications. We take a systematic approach to address several key challenges, which are unsolvable by synthesizing out-of-box techniques, from ground up. Extensive evaluations show that our system can achieve stable archiving throughput of 350 Mb/s with one core, and saturate a 1 Gb/s link with four cores; for a real trace, it outperforms a baseline system without any of our designs by over 110× .

With the proliferation of the Internet of Things (IoT), wireless sensor networks, mobile social networks, crowdsensing applications, and beyond, huge amounts of data are being explosively generated every day. While big data and IoT bring well-understood benefits to increase knowledge and improve the quality of our daily lives, they also face wide attacking surfaces, and thus raise critical concerns on notions of trust, security, and privacy. One of the fundamental questions is how to collect, store, and process the huge amounts of data in a secure and privacy-preserving manner, without compromising the potential of capitalization on big data and IoT.

The ever-growing of Internet of Things (IoT) data and the new spectrum of data applications have stimulated IoT clients to outsource their data to cloud servers or datacenters. Apart from storage service, the IoT clients also desires the servers to execute functional operations per client's request. In this paper, we aim to design the secure mechanisms that allow the IoT clients to outsource their encrypted data to geographically distributed servers while supporting homomorphic computation functions. We leverage the distributed index framework to disassemble and spread data evenly across geographically distributed servers while employing the key-value store as the underlying structure for fast data retrieval. To support computing over encrypted data, we customize Shamir's secret sharing into our mechanisms to design a tunable scheme for the adaption of different IoT application scenarios. In particular, we design three tunable protocols to achieve the effective additive homomorphic computations while approaching efficiency in terms of servers utilization, computation, and storage overhead. Even the designs focus on the additive computation, we show that it can be readily extended to other types of homomorphic computations as well as verifying the correctness of stored data. Based on the proposed protocols, we design system prototypes, deploy them in Amazon Web services, and evaluate our construction experimentally. Through experimental results, we show that our designs can achieve the efficiency in various perspectives.

Spectrum auction is an effective way to redistribute scarce spectrum resources. However, most spectrum auction designs only target at economic robustness, while neglecting the inherent privacy leakage problem. Existing secure spectrum auction mechanisms fail to provide adequate security, and they all neglect the online fashion of spectrum request arrival. In this paper, for the first time, we propose a Privacy-pReserving and truthful Online double auction mechanism for Spectrum allocaTion in wireless networks, PROST. Compared with the state-of-the-art solutions, PROST provides a comprehensive and strong protection for users' sensitive information, especially for location privacy and time dynamics. PROST is constructed based on our carefully designed security building blocks, which support various arithmetics over encrypted real numbers, and they are also well applicable in other spectrum auctions. Besides, we improve on the existing online spectrum auction mechanisms by designing a novel privacy-preserving buyer grouping protocol for spectrum reuse. We not only theoretically prove that PROST can realize an all-round security against semi-honest adversaries but also extensively evaluate its performance. Experimental results validate that PROST achieves nice spectrum allocation efficiency with light computation and communication costs.

Auction is an effective way to allocate goods or services to bidders who value them the most. The rapid growth of e-auctions facilitates online transactions but poses new and distinctive challenges. It is difficult to establish trust among sellers, buyers, and auctioneers without centralized auction websites or platforms (the auctioneer) which collect bids and derive the auction results. However, these third parties may be untrustworthy, and malicious sellers or buyers may refuse to deliver the goods or payment according to the protocol. Moreover, the open and anonymous online environment may stimulate auction participants to form collusion coalitions to rig the auction and reap unfair profit. Many auction designs have been proposed to address these concerns, but they fall short of simultaneously achieving decentralization (i.e., held without a trusted third utility), strong consensus (i.e., the establishment of trust), collusion resistance, and practical implementation. We present CReam, the first decentralized collusion-resistant e-auction system that is implemented with smart contract on the blockchain. With the carefully designed smart auction contract, mutually distrustful and rational sellers and buyers are stimulated to operate properly, hence transact safely without trusted third parties. The auction mechanism in the smart contract can effectively prevent bidder collusion and realize economic robustness, i.e., truthfulness. We implement a fully functional CReam on the Ethereum network. Extensive experimental results confirm that CReam can greatly reduce the probability of collusion and achieve an approximate optimal revenue at a low cost of contract execution.

To accommodate massive digital data, distributed data stores have become the main solution for cloud services. Among others, key-value stores are widely adopted due to their superior performance. But with the rapid growth of cloud storage, there are growing concerns about data privacy. In this paper, we design and build EncKV, an encrypted and distributed key-value store with rich query support. First, EncKV partitions data records with secondary attributes into a set of encrypted key-value pairs to hide relations between data values. Second, EncKV uses the latest cryptographic techniques for searching on encrypted data, i.e., searchable symmetric encryption (SSE) and order-revealing encryption (ORE) to support secure exact-match and range-match queries, respectively. It further employs a framework for encrypted and distributed indexes supporting query processing in parallel. To address inference attacks on ORE, EncKV is equipped with an enhanced ORE scheme with reduced leakage. For practical considerations, EncKV also enables secure system scaling in a minimally intrusive way. We complete the prototype implementation and deploy it on Amazon Cloud. Experimental results confirm that EncKV preserves the efficiency and scalability of distributed key-value stores.

Image denoising via machine learning techniques, particularly neural networks, has been shown to achieve state-of-the-art performance. However, in practice security and privacy issues undesirably arise in applying a trained machine learning model to image denoising. In this paper, we propose a system framework that enables the owner of a trained machine learning model to provide secure image denoising service to an authorized user, via the aid of cloud computing. Our framework ensures that the cloud server learns nothing about the model and the user's images, while the user learns nothing about the model except denoised images. Experiments are conducted for performance evaluation, and the results show that our design can achieve denoising quality close to that in the plaintext domain. For future work, we plan to explore various directions for optimizing the runtime performance.

Leveraging the wisdom of crowd for knowledge discovery and monetization is increasingly popular nowadays. Among others, one popular way of leveraging the crowd wisdom is crowdsensing with truth discovery, which is able to discover truthful knowledge from the unreliable sensory data harvested from mobile clients. In order to become truly successful, however, a number of challenges are yet to be addressed. First, safeguarding clients' sensory data is demanded for privacy protection. Second, in many real crowdsensing applications, data are usually collected in a streaming manner, so truth discovery is naturally required to be efficiently conducted in a streaming fashion. Thirdly, knowledge monetization should be made full-fledged, endowed with features of transparency and streamlined processing while fully addressing the practical needs of parties in the monetization ecosystem. In this paper, we present our initial effort on a crowdsensing framework that enables privacy-preserving knowledge discovery and full-fledged blockchain-based knowledge monetization. Our framework enables privacy-preserving and efficient truth discovery over encrypted crowdsensed data streams for truthful knowledge discovery. Meanwhile, with careful integration of the newly emerging blockchain-based smart contract technology, our framework allows full-fledged knowledge monetization. Tackling the challenges of monetization fairness and (on-chain) knowledge confidentiality, our customized knowledge monetization design well respects the interests of knowledge seller and requester, with full support of transparency, streamlined processing, and automatic quality-aware rewards for clients. Extensive experiments on Microsoft Azure cloud and Ethereum blockchain demonstrate the practically affordable performance of our design.

Android is the primary target for mobile malware. To protect users, phone vendors (e.g., Samsung and Huawei) usually leverage third-party security service providers (e.g., VirusTotal and Qihoo 360) to detect malicious apps in app stores and collect apps' runtime behaviors on users' phones to further spot malware missed in the previous step. However, this practice could cause privacy concerns to phone vendors, users and security service providers. Specifically, phone vendors do not want to share apps (including the paid ones) with security service providers, while the latter do not want to share the malware signatures with the former. Moreover, users do not want to expose apps' runtime behaviors to third parties. These concerns would cause a real dilemma for each involved party. In this paper, we propose a privacy-preserving malware detection system for Android, in which the privacy (or assets) of phone vendors, users, and security service providers are protected. It detects malicious apps in phone vendor's app stores and on users' phones, without directly sharing apps, apps' runtime behaviors, and malware signatures to other parties. We implement a prototype system called PPMDroid and apply several optimizations to save bandwidth and speed up the process. Extensive evaluation results with real malware samples demonstrate the effectiveness and efficiency of our system.

Enabling search directly over encrypted data is a desirable technique to allow users to effectively utilize encrypted data outsourced to a remote server like cloud service provider. So far, most existing solutions focus on an honest-but-curious server, while security designs against a malicious server have not drawn enough attention. It is not until recently that a few works address the issue of verifiable designs that enable the data owner to verify the integrity of search results. Unfortunately, these verification mechanisms are highly dependent on the specific encrypted search index structures, and fail to support complex queries. There is a lack of a general verification mechanism that can be applied to all search schemes. Moreover, no effective countermeasures (e.g., punishing the cheater) are available when an unfaithful server is detected. In this work, we explore the potential of smart contract in Ethereum, an emerging blockchain-based decentralized technology that provides a new paradigm for trusted and transparent computing. By replacing the central server with a carefully-designed smart contract, we construct a decentralized privacy-preserving search scheme where the data owner can receive correct search results with assurance and without worrying about potential wrongdoings of a malicious server. To better support practical applications, we introduce fairness to our scheme by designing a new smart contract for a financially-fair search construction, in which every participant (especially in the multiuser setting) is treated equally and incentivized to conform to correct computations. In this way, an honest party can always gain what he deserves while a malicious one gets nothing. Finally, we implement a prototype of our construction and deploy it to a locally simulated network and an official Ethereum test network, respectively. The extensive experiments and evaluations demonstrate the practicability of our decentralized search scheme over encrypted data.

In crowd sensing, truth discovery (TD) refers to finding reliable information from noisy/biased data collected from different providers. To protect providers' data while enabling truth distillation, privacy-preserving truth discovery (PPTD) has received wide attention recently. However, all existing approaches require iterative interaction between server(s) and individual providers, which inevitably demand all providers to be always online. Otherwise, the protocol would fail or expose extra provider information. In this paper, we design and implement the first non-interactive PPTD system that completely removes the online requirement with strong privacy guarantees. Our framework follows the same two-server model from the best-known prior solution, and leverages Yao's Garbled Circuit (GC). Yet, we devise non-trivial speedup techniques for TD-optimized implementation. Firstly, we identify reusable computations in TD to accelerate the circuit generation. Secondly, we securely evaluate the burdensome non-linear functions in TD via customized approximation with accuracy and improved efficiency. Thirdly, we reduce the online execution time by bridging together latest advancements of component-based GC and various computations needed in TD. Unlike prior arts, our framework does not reveal any intermediate results, and further supports “late-join” providers without protocol suspension/restart. The practical performance of our proof-of-concept implementation is verified through extensive evaluations.

Data deduplication is able to effectively identify and eliminate redundant data and only maintain a single copy of files and chunks. Hence, it is widely used in distributed storage systems and cloud storage to save the users' network bandwidth for uploading files. However, the occurrence of deduplication can be easily identified by monitoring and analyzing network traffic, which leads to the risk of user privacy leakage. An attacker can carry out a very dangerous side channel attack, i.e., learn-the-remaining-information (LRI) attack, to reveal users' privacy information by exploiting the side channel of network traffic in deduplication. Existing work addresses the LRI attack at the cost of the high bandwidth consumption. In order to address this problem, we propose a simple yet effective scheme, called randomized redundant chunk scheme (RRCS), to significantly mitigate the risk of the LRI attack while maintaining the high bandwidth efficiency of deduplication. The idea behind RRCS is to add randomized redundant chunks to mix up the real deduplication states of files used for the LRI attack, which effectively obfuscates the view of the attacker, who attempts to exploit the side channel of network traffic for the LRI attack. Our security analysis shows that RRCS significantly mitigates the risk of the LRI attack. We have implemented the RRCS prototype and evaluated it by using three real-world datasets. Experimental results demonstrate RRCS significantly outperforms existing work in terms of bandwidth efficiency.

Over the past few years, enterprises start adopting software middlebox services from cloud or NFV service providers. Although this new service model is recognized to be cost-effective and scalable for traffic processing, privacy concerns arise because of traffic redirection to outsourced middleboxes. To ease these concerns, recent efforts are made to design secure middlebox services that can directly function over encrypted traffic and middlebox rules. But prior designs only work for portions of frequently-used network functions. To push forward this area, in this work, we investigate header matching based functions like firewall filtering and packet classification. To enable privacy-preserving processing on encrypted packets, we start from the latest primitive “order-revealing encryption (ORE)” for encrypted range search. In particular, we devise a new practical ORE construction tailored for network functions. The advantages include: 1) guaranteed protection of packet headers and rule specified ranges; 2) reduced accessible information during comparisons; 3) rule-aware size reduction for ORE ciphertexts. We implement a fully functional system prototype and deploy it at Microsoft Azure Cloud. Evaluation results show that our system can achieve per packet matching latency 0.53 to 15.87 millisecond over 1.6K firewall rules.

Images are becoming one of the key enablers of user connectivity in social media applications. Many of them are directly exploring image content to suggest new friends with similar interests. To handle the explosive volumes of images, one common trend is to leverage the public cloud as their robust service backend. Despite the convenience, exposing content-rich images to the cloud inevitably raises acute privacy concerns. In this paper, we propose a privacy-preserving architecture for image-centric social discovery services, designed to function over encrypted images. We first adopt the effective Bag-of-Words model to extract the “visual content” of users' images into respective image profile vectors. We then model the core problem as similarity retrieval of encrypted high-dimensional vectors. To achieve scalable services over millions of encrypted images, we design a secure and efficient index structure, which enables practical and accurate social discovery from the cloud, without revealing any image profile or image content. For completeness, we further enrich our service with secure updates, facilitating user's image update. Our implementation is deployed at an Android phone and Amazon Cloud, and extensive experiments are conducted on a large Flickr image dataset which demonstrates the desired quality of services.

Verifiable pattern matching is the problem of finding a given pattern verifiably from the outsourced textual data, which is resident in an untrusted remote server. This problem has drawn much attention due to a large number of applications. The state-of-the-art method for this problem suffers from low efficiency. To enable fast verifiable pattern matching, we propose a novel scheme in this paper. Our scheme is based on an ordered set accumulator data structure and a newly developed verifiable suffix array structure, which only involves fast cryptographic hash computations. Our scheme also supports fast multiple-occurrence pattern matching. A striking feature of our proposed scheme is that our scheme works even with no secret keys, which ensures public verifiability. We conduct extensive experiments to evaluate the proposed scheme using Java. The results show that our scheme is orders of magnitude faster than the state-of-the-art work. Specifically, our scheme with public verifiability only costs a preprocessing time of 47 s (merely one-time off-line cost during outsourcing), a search time of 30 μs, a verification time of 149 μs, and a proof size of 2760 bytes for a verifiable pattern matching query with pattern length 200 on 10-million long textual data which consists of sequences of two-byte, Unicode characters in Java.

Mobile crowdsensing enables convenient sensory data collection from a large number of mobile devices and has found various applications. In the real practice, however, the sensory data collected from various mobile devices are usually unreliable. To extract truthful information from the unreliable sensory data in mobile crowdsensing, the topic of truth discovery has received wide attention recently, which essentially operates by estimating user reliability degrees and performing reliability-aware truthful aggregation. Despite the effectiveness, applying truth discovery in mobile crowdsensing faces several privacy and security challenges. First, the sensory data and reliability degrees of users may reveal privacy-sensitive information and, thus, demand strong protection. Second, the requester that initiates a crowdsensing application usually needs to have monetary investment, so the inferred truths can be the requester's proprietary information and should be protected as well. In this paper, we propose a new system architecture enabling encrypted truth discovery in mobile crowdsensing. We focus on general and realistic mobile crowdsensing scenarios with varying levels of user participation, and our security design is built on the confidence-aware truth discovery (CATD) approach for its state-of-the-art accuracy in such scenarios. In our system architecture, users send encrypted sensory data to the cloud, where CATD is then conducted in the encrypted domain. The final encrypted inferred truths are sent to the requester for decryption. Along the whole workflow, the sensory data and reliability degrees of users, as well as the inferred truths of the requester, are kept private. Extensive experiments over real-world mobile crowdsensing dataset show that our design achieves practical performance on mobile devices.

In order to ease the burden of the in-house integrated circuit (IC) design, cloud-based IC design platforms advance rapidly, bringing benefits such as reduced capital costs and convenient design collaboration. However, such migration raises security challenges on IC Intellectual Property (IP) protection. Sensitive design data is unwillingly exposed to the cloud. In this paper, we initiate the first study for secure cloud-based IC design, and propose a hybrid cloud framework for privacy-assured IC timing analysis, i.e., an expensive procedure in the IC design flow for circuit delay evaluation. Our key observation is that more and more IP blocks are universally reused. After carefully extracting a small portion of sensitive blocks from the circuit, our framework only outsources non-sensitive design data to the public cloud. However, that “data splitting” hinders sequential delay evaluation. We then develop algorithms to enable the public cloud to derive intermediate results from non-sensitive data, which can be integrated with sensitive data at the private cloud. Additionally, we devise a practical verification protocol to assure the integrity of outsourced computation. Security analysis shows that our design is resilient to IC reverse engineering. Evaluations over large IC benchmarks demonstrate its efficiency and effectiveness.

Searchable Symmetric Encryption (SSE) has been widely studied in cloud storage, which allows cloud services to directly search over encrypted data. Most SSE schemes only work with honest-but-curious cloud services that do not deviate from the prescribed protocols. However, this assumption does not always hold in practice due to the untrusted nature in storage outsourcing. To alleviate the issue, there have been studies on Verifiable Searchable Symmetric Encryption (VSSE), which functions against malicious cloud services by enabling results verification. But to our best knowledge, existing VSSE schemes exhibit very limited applicability, such as only supporting static database, demanding specific SSE constructions, or only working in the single-user model. In this paper, we propose GSSE, the first generic verifiable SSE scheme in the single-owner multiple-user model, which provides verifiability for any SSE schemes and further supports data updates. To generically support result verification, we first decouple the proof index in GSSE from SSE. We then leverage Merkle Patricia Tree (MPT) and Incremental Hash to build the proof index with data update support. We also develop a timestamp-chain for data freshness maintenance across multiple users. Rigorous analysis and experimental evaluations show that GSSE is secure and introduces small overhead for result verification.

Distributed data stores have been rapidly evolving to serve the needs of large-scale applications such as online gaming and real-time targeting. In particular, distributed key-value stores have been widely adopted due to their superior performance. However, these systems do not guarantee to provide strong protection of data confidentiality, and as a result fall short of addressing serious privacy concerns raised from massive data breaches.

Data deduplication [3] is able to effectively identify and eliminate redundant data and only maintain a single copy of files and chunks. Hence, it is widely used in cloud storage systems to save the users' network bandwidth for uploading data. However, the occurrence of deduplication can be easily identified by monitoring and analyzing network traffic, which leads to the risk of user privacy leakage. The attacker can carry out a very dangerous side channel attack, i.e., learn-the-remaining-information (LRI) attack, to reveal users' privacy information by exploiting the side channel of network traffic in deduplication [1]. In the LRI attack, the attacker knows a large part of the target file in the cloud and tries to learn the remaining unknown parts via uploading all possible versions of the file's content. For example, the attacker knows all the contents of the target file X except the sensitive information θ. To learn the sensitive information, the attacker needs to upload m files with all possible values of θ, respectively. If a file Xd with the value θd is deduplicated and other files are not, the attacker knows that the information θ = θd.

Emerging decentralized storage services such as Storj and Filecoin show promise as a new paradigm for data outsourcing. These services tie cryptocurrency to personal storage resources and leverage blockchain technology to ensure data integrity in distributed networks. Compared to current cloud storage, they are expected to be more scalable, cost effective, and secure. In addition to the features above, strong guarantees of data privacy are seriously desired due to today's prevalent data leak and abuse incidents. However, simply using end-to-end encryption limits the search capability and thus will degrade the user experience. In this paper, we propose an encrypted decentralized storage architecture that can support trustworthy and private keyword search functions. We start from searchable encryption to achieve search on encrypted data. Yet, only adopting this primitive is not sufficient to address particular threats in our target decentralized service model. Service peers would maliciously return incorrect results, while user peers would fraudulently refuse to pay service fees. To resolve those threats, we devise specific secure data addition and keyword search protocols to enable client-side verifiability and blockchain based fair judgments on the search results. For practical considerations, we integrate an efficient dynamic searchable encryption scheme to our protocols as an instantiation to lower the blockchain overhead. Our security and performance analysis indicates the advance of the proposed architecture.

Voice, as a convenient and efficient way of information delivery, has a significant advantage over the conventional keyboard-based input methods, especially on small mobile devices such as smartphones and smartwatches. However, the human voice could often be exposed to the public, which allows an attacker to quickly collect sound samples of targeted victims and further launch voice impersonation attacks to spoof those voice-based applications. In this paper, we propose the design and implementation of a robust software-only voice impersonation defense system, which is tailored for mobile platforms and can be easily integrated with existing off-the-shelf smart devices. In our system, we explore magnetic field emitted from loudspeakers as the essential characteristic for detecting machine-based voice impersonation attacks. Furthermore, we use a state-of-the-art automatic speaker verification system to defend against human imitation attacks. Finally, our evaluation results show that our system achieves simultaneously high accuracy (100%) and low equal error rates (EERs) (0%) in detecting the machine-based voice impersonation attack on smartphones.

Locality sensitive hashing (LSH) is an efficient method for solving the problem of approximate similarity search in high-dimensional spaces. Through LSH, a high-dimensional similarity join can be processed in the same way as hash join, making the cost of joining two large datasets linear. By judicially analyzing the properties of multiple LSH algorithms, we propose a generic method to accelerate the process of joining two large datasets using LSH. The crux of our method lies in the way we identify a set of representative points to reduce the number of LSH lookups. Theoretical analyses show that our proposed method can greatly reduce the number of lookup operations and retain the same result accuracy compared to executing LSH lookups for every query point. Furthermore, we demonstrate the generality of our method by showing that the same principle can be applied to LSH algorithms for three different metrics: the Euclidean distance (QALSH), Jaccard similarity measure (MinHash), and Hamming distance (sequence hashing). Results from experimental studies using real datasets confirm our error analyses and show significant improvements of our method over the state-of-the-art LSH method: to achieve over 0.95 recall, we only need to operate LSH lookups for at most 15% of the query points.

Similarity-oriented services serve as a foundation in a wide range of data analytic applications such as machine learning, target advertising, and real-time decisions. Both industry and academia strive for efficient and scalable similarity discovery and querying techniques to handle massive, complex data records in the real world. In addition to performance, data security and privacy become an indispensable criterion in the quality of service due to progressively increased data breaches. To address this serious concern, in this paper, we propose and implement “EncSIM”, an encrypted and scalable similarity search service. The architecture of EncSIM enables parallel query processing over distributed, encrypted data records. To reduce client overhead, EncSIM resorts to a variant of the state-of-the-art similarity search algorithm, called all-pairs locality-sensitive hashing (LSH). We describe a novel encrypted index construction for EncSIM based on searchable encryption to guarantee the security of service while preserving performance benefits of all-pairs LSH. Moreover, EncSIM supports data record addition with a strong security notion. Intensive evaluations on a cluster of Redis demonstrate low client cost, linear scalability, and satisfied query performance of EncSIM.

Distributed key-value stores are rapidly evolving to serve the needs of high-performance web services and large-scale cloud computing applications. It is desirable to search directly over an encrypted key value (KV) store, as data is increasingly stored in the cloud. Encrypted, distributed and searchable key- value stores have been the focus of research, where a data owner outsources his key-value store to a remote server in the cloud in the encrypted form, yet still keeping it searchable. In this paper, we explore the encrypted KV store with the secure multi-client query support. In particular, the data owner can authorize multiple trustable clients (third parties) and allow them to search its encrypted database over KV store. The design goal is to ensure the data confidentiality and query privacy. From the data owner's perspective, the authorized query should not leak too much information thus causing threats to its private database. From clients' perspective, they have the explicit requirement that the query values should not be exposed to the data owner. We design two encryption schemes and token generation methods to satisfy different requirements. To validate the effciency of our protocols, we implement the system prototype to evaluate their performance.

Searchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users' search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.

Similarity search on high-dimensional data has been intensively studied for data processing and analytics. Despite its broad applicability, data security and privacy concerns along the trend of data outsourcing have not been fully addressed. In this paper, we investigate privacy-preserving similarity join queries, i.e., a pivotal primitive of similarity search that finds pairwise similar data points across two data sets. We start from locality-sensitive hashing and searchable symmetric encryption, i.e., the most practical techniques for similarity search and encrypted search, respectively. However, the immediate combination of two techniques discloses the distribution of the query set, which is exploitable to compromise the confidentiality of queries. To enhance the security, we propose the frequency hiding query scheme, which allows the server to see the flattened query distribution only. To improve the scalability, we further design the result sharing query scheme, which processes a small portion of query points and shares the results with other nearby points. Besides, we set up a strict constraint to carefully select query points to achieve “as-strong-as-possible” guarantees. We formalize the leakage functions in the context of similarity joins, and conduct rigorous security analysis. We implement and evaluate the proposed query schemes on Azure cloud. Experimental results indicate that they have different tradeoffs on security, efficiency, and accuracy, which can flexibly be used for different deployment scenarios.

Along with the rapid advancement of digital image processing technology, image denoising remains a fundamental task, which aims to recover the original image from its noisy observation. With the explosive growth of images on the Internet, one recent trend is to seek high quality similar patches at cloud image databases and harness rich redundancy therein for promising denoising performance. Despite the well-understood benefits, such a cloud-based denoising paradigm would undesirably raise security and privacy issues, especially for privacy-sensitive image data sets. In this paper, we initiate the first endeavor toward privacy-preserving image denoising from external cloud databases. Our design enables the cloud hosting encrypted databases to provide secure query-based image denoising services. Considering that image denoising intrinsically demands high quality similar image patches, our design builds upon recent advancements on secure similarity search, Yao's garbled circuits, and image denoising operations, where each is used at a different phase of the design for the best performance. We formally analyze the security strengths. Extensive experiments over real-world data sets demonstrate that our design achieves the denoising quality close to the optimal performance in plaintext.

Locality sensitive hashing (LSH) is an efficient method for solving the problem of approximate similarity search in highdimensional spaces. Through LSH, a high-dimensional similarity join can be processed in the same way as hash join, making the cost of joining two large datasets linear. By judicially analyzing the properties of multiple LSH algorithms, we propose a generic method to speed up the process of joining two large datasets using LSH. The crux of our method lies in the waywhich we identify a set of representative points to reduce the number of LSH lookups. Theoretical analyzes show that our proposed method can greatly reduce the number of lookup operations and retain the same result accuracy compared to executing LSH lookups for every query point. Furthermore, we demonstrate the generality of our method by showing that the same principle can be applied to LSH algorithms for three different metrics: the Euclidean distance (QALSH), Jaccard similarity measure (MinHash), and Hamming distance (sequence hashing). Results from experimental studies using real datasets confirm our error analyzes and show significant improvements of our method overthe state-of-the-art LSH method: to achieve over 0.95 recall, we only need to operate LSH lookups for at most 15 percent of the query points.

Nowadays, large volumes of multimedia data are outsourced to the cloud to better serve mobile applications. Along with this trend, highly correlated datasets can occur commonly, where the rich information buried in correlated data is useful for many cloud data generation/dissemination services. In light of this, we propose to enable a secure and efficient cloud-assisted image sharing architecture for mobile devices, by leveraging outsourced encrypted image datasets with privacy assurance. Different from traditional image sharing, we aim to provide a mobile-friendly design that saves the transmission cost for mobile clients, by directly utilizing outsourced correlated images to reproduce the image of interest inside the cloud for immediate dissemination. First, we propose a secure and efficient index design that allows the mobile client to securely find from encrypted image datasets the candidate selection pertaining to the image of interest for sharing. We then design two specialized encryption mechanisms that support secure image reproduction from encrypted candidate selection. We formally analyze the security strength of the design. Our experiments explicitly show that both the bandwidth and energy consumptions at the mobile client can be saved, while achieving all service requirements and security guarantees.

The explosive growth of multimedia contents, especially videos, is pushing forward the paradigm of cloud-based media hosting today. However, the wide attacking surface of the public cloud and the growing security awareness from the society are both calling for data encryption before outsourcing to cloud. Under the circumstance of encrypted videos, how to still preserve all the service benefits of cloud media center remains to be fully explored. In this paper, we present a secure system architecture design as our initial effort toward this direction, which bridges together the advancements of video coding techniques and secure deduplication. Our design enables the cloud with the crucial deduplication functionality to completely eliminate the extra storage and bandwidth cost, which would have been incurred by hosting encrypted videos from different entities. The design is also carefully tailored to the scalable video coding (SVC) techniques to support heterogeneous networks and devices for high-quality adaptive video dissemination. We show fully functional system implementations with structure-aware encryption design and structure-aware deduplication strategies that are both completely compliant with the video format in SVC. Extensive security analysis and experiments via our prototype deployed on Azure cloud platform show the practicality of the design. Our work can also be easily extended to support other media applications that employ media files with scalable structures.

Modern distributed key-value stores are offering superior performance, incremental scalability, and fine availability for data-intensive computing and cloud-based applications. Among those distributed data stores, the designs that ensure the confidentiality of sensitive data, however, have not been fully explored yet. In this paper, we focus on designing and implementing an encrypted, distributed, and searchable key-value store. It achieves strong protection on data privacy while preserving all the above prominent features of plaintext systems. We first design a secure data partition algorithm that distributes encrypted data evenly across a cluster of nodes. Based on this algorithm, we propose a secure transformation layer that supports multiple data models in a privacy-preserving way, and implement two basic APIs for the proposed encrypted key-value store. To enable secure search queries for secondary attributes of data, we leverage searchable symmetric encryption to design the encrypted secondary indexes which consider security, efficiency, and data locality simultaneously, and further enable secure query processing in parallel. For completeness, we present formal security analysis to demonstrate the strong security strength of the proposed designs. We implement the system prototype and deploy it to a cluster at Microsoft Azure. Comprehensive performance evaluation is conducted in terms of Put/Get throughput, Put/Get latency under different workloads, system scaling cost, and secure query performance. The comparison with Redis shows that our prototype can function in a practical manner.

Migrating middleboxes to third-party service providers (e.g., clouds and ISPs) has drawn widespread attentions recently from both industry and academia. While its benefits on reduced local cost and increased service scalability are well understood, such deployment also introduces new security concerns, due to the fact that these boxes are no longer under the direct control of enterprises. Among others, one fundamental desideratum here is to ensure that those middleboxes consistently perform network functions as intended. In this work, we propose practical solutions towards enabling runtime execution assurances of outsourced middleboxes with high confidence. As an initial effort, we target on pattern matching based network functions, which cover a broad class of middlebox applications such as instruction detection, web firewall, and traffic classification. For efficiency, our design follows the same roadmap of probabilistic checking that provides tunable levels of assurance, as in outsourced computation and distributed computing literature. We show how to synthesize the design intuitions in the context of outsourced middleboxes and the dynamic network effect. We present diligent technical instantiations, in the case of single middlebox and the composition of multiple middlebox service chaining, respectively. For a large batch of packets, sufficiently high assurance levels can be achieved by pre-processing only a few randomly selected packets, with marginal overhead. Evaluations of our system prototype on Amazon EC2 show that, the processing of 1000 packets, which includes pattern matching and execution proof generation, results in 200–500ms latency and throughput up to 360Mbps.

Outsourced middlebox services have drawn broad attentions recently from both industry and academia [1]. Despite benefiting enterprises from reduced cost and increased service scalability, such services also introduce acute security concerns, because these boxes are no longer under direct control of enterprises. Among others, one fundamental and immediate requirement is to ensure that those middleboxes always perform network functions truthfully and correctly [2]. Fulfilling this requirement will extend enterprises' visibility into remote middleboxes and promote further adoption of middlebox outsourcing services. Unfortunately, to our best knowledge, little work investigates the above problem, i.e., making network functions executed by middleboxes verifiable.

Near-duplicate detection (NDD) plays an essential role for effective resource utilization and possible traffic alleviation in many emerging network architectures, leveraging in-network storage for various content-centric services. As innetwork storage grows, data security has become one major concern. Though encryption is viable for in-network data protection, current techniques are still lacking for effectively locating encrypted near-duplicate data, making the benefits of NDD practically invalidated. Besides, adopting encrypted innetwork storage further complicates the user authorization when locating near-duplicate data from multiple content providers under different keys. In this paper, we propose a secure and effective NDD system over encrypted in-network storage supporting multiple content providers. Our design bridges locality-sensitive hashing (LSH) with a newly developed cryptographic primitive, multi-key searchable encryption, which allows the user to send only one encrypted query to access near-duplicate data encrypted under different keys. It relieves the users from multiple rounds of interactions or sending multiple different queries respectively. As simply applying LSH does not ensure the detection quality, we then leverage Yao's garbled circuits to build a secure protocol to obtain highly accurate results, without user-side post-processing. We formally analyze the security strength. Experiments demonstrate our system achieves practical performance with comparable accuracy to plaintext.

Middleboxes are essential for a wide range of advanced traffic processing in modern enterprise networks. Recent trend of deploying middleboxes in cloud as virtualized services further expands potential benefits of middleboxes while avoiding local maintenance burdens. Despite promising, designing outsourced middleboxes still faces several security challenges. First, many middlebox processing services, like intrusion detection, require packet payload inspection, while the ever-increasing adoption of HTTPS limits the function due to the end-to-end encryption. Second, many packet inspection rules used by middleboxes can be proprietary in nature. They may contain sensitive information of enterprises, and thus need strong protection when configuring middleboxes in untrusted outsourced environments. In this paper, we propose a practical system architecture for outsourced middleboxes to perform deep packet inspection over encrypted traffic, without revealing either packet payloads or inspection rules. Our first design is an encrypted high-performance rule filter that takes randomized tokens from packet payloads for encrypted inspection. We then elaborate through carefully tailored techniques how to comprehensively support open-source real rulesets. We formally analyze the security strength. Implementations at Amazon Cloud show that our system introduces roughly 100 millisecond latency in each connection initialization, with individual processing throughput over 3500 packets/second for 500 concurrent connections.

In-network content caching has been a natural trend in emerging network architectures to handle the exponential growth of video traffic. However, due to the potentially wide attacking surfaces, caching video content in the increasingly untrusted networked environment inevitably raises new concerns on user privacy exposure and unauthorized video access. Existing encrypted protocols like HTTPs either fall short of fully leveraging in-network caching or require decrypting the traffic in the middle without guaranteeing the end-to-end security. In this paper, we present a new networked system for efficient encrypted video delivery while preserving the benefits of in-network caching. As video chunks are encrypted before distribution, we first design a compact, efficient, yet encrypted video fingerprint index to empower the network with a fully controlled capability of locating the cached encrypted chunks for given encrypted requests. We then explain how to deploy the encrypted design in our proposed architecture and present a secure redundancy elimination protocol to enable fast video delivery via leveraging cached encrypted chunks. We further discuss the full support of cache management, adaptive video delivery, and video access control. Rigorous analysis and prototype evaluations demonstrate the security, efficiency, and effectiveness of the design.

Cloud computing enables an economically promising paradigm of computation outsourcing. However, how to protect customers confidential data processed and generated during the computation is becoming the major security concern. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. Our mechanism design explicitly decomposes LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computation than the general circuit representation. Specifically, by formulating private LP problem as a set of matrices/vectors, we develop efficient privacy-preserving problem transformation techniques, which allow customers to transform the original LP into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP and derive the necessary and sufficient conditions that correct results must satisfy. Such result verification mechanism is very efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design.

Key-exposure resistance has always been an important issue for in-depth cyber defence in many security applications. Recently, how to deal with the key exposure problem in the settings of cloud storage auditing has been proposed and studied. To address the challenge, existing solutions all require the client to update his secret keys in every time period, which may inevitably bring in new local burdens to the client, especially those with limited computation resources, such as mobile phones. In this paper, we focus on how to make the key updates as transparent as possible for the client and propose a new paradigm called cloud storage auditing with verifiable outsourcing of key updates. In this paradigm, key updates can be safely outsourced to some authorized party, and thus the key-update burden on the client will be kept minimal. In particular, we leverage the third party auditor (TPA) in many existing public auditing designs, let it play the role of authorized party in our case, and make it in charge of both the storage auditing and the secure key updates for key-exposure resistance. In our design, TPA only needs to hold an encrypted version of the client's secret key while doing all these burdensome tasks on behalf of the client. The client only needs to download the encrypted secret key from the TPA when uploading new files to cloud. Besides, our design also equips the client with capability to further verify the validity of the encrypted secret keys provided by the TPA. All these salient features are carefully designed to make the whole auditing procedure with key exposure resistance as transparent as possible for the client. We formalize the definition and the security model of this paradigm. The security proof and the performance simulation show that our detailed design instantiations are secure and efficient.

2D barcodes have enjoyed a significant penetration rate in mobile applications. This is largely due to the extremely low barrier to adoption-almost every camera-enabled smartphone can scan 2D barcodes. As an alternative to NFC technology, 2D barcodes have been increasingly used for security-sensitive mobile applications including mobile payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. Due to the visual nature, 2D barcodes are subject to eavesdropping when they are displayed on the smartphone screens. On the other hand, the fundamental design principles of 2D barcodes make it difficult to add security features. In this paper, we propose SBVLC-a secure system for barcode-based visible light communication (VLC) between smartphones. We formally analyze the security of SBVLC based on geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen view angles and leveraging user-induced motions. We then develop three secure data exchange schemes that encode information in barcode streams. These schemes are useful in many security-sensitive mobile applications including private information sharing, secure device pairing, and contactless payment. SBVLC is evaluated through extensive experiments on both Android and iOS smartphones.

As e-health technology continues to advance, health related multimedia data is being exponentially generated from healthcare monitoring devices and sensors. Coming with it are the challenges on how to efficiently acquire, index, and process such a huge amount of data for effective healthcare and related decision making, while respecting user's data privacy. In this paper, we propose a secure cloud-based framework for privacy-aware healthcare monitoring systems, which allows fast data acquisition and indexing with strong privacy assurance. For efficient data acquisition, we adopt compressive sensing for easy data sampling, compression, and recovery. We then focus on how to secure and fast index the resulting large amount of continuously generated compressed samples, with the goal to achieve secure selected retrieval over compressed storage. Among others, one particular challenge is the practical demand to cope with the incoming data samples in high acquisition rates. For that problem, we carefully exploit recent efforts on encrypted search, efficient content-based indexing techniques, and fine-grained locking algorithms, to design a novel encrypted index with high-performance customization. It achieves memory efficiency, provable security, as well as greatly improved building speed with nontrivial multithread support. Comprehensive evaluations on Amazon Cloud show that our encrypted design can securely index 1 billion compressed data samples within only 12 min, achieving a throughput of indexing almost 1.4 million encrypted samples per second. Accuracy and visual evaluation on a real healthcare dataset shows good quality of high-value retrieval and recovery over encrypted data samples.

The image and multimedia data produced by individuals and enterprises is increasing every day. Motivated by the advances in cloud computing, there is a growing need to outsource such computational intensive image feature detection tasks to cloud for its economic computing resources and on-demand ubiquitous access. However, the concerns over the effective protection of private image and multimedia data when outsourcing it to cloud platform become the major barrier that impedes the further implementation of cloud computing techniques over massive amount of image and multimedia data. To address this fundamental challenge, we study the state-of-the-art image feature detection algorithms and focus on Scalar Invariant Feature Transform (SIFT), which is one of the most important local feature detection algorithms and has been broadly employed in different areas, including object recognition, image matching, robotic mapping, and so on. We analyze and model the privacy requirements in outsourcing SIFT computation and propose Secure Scalar Invariant Feature Transform (SecSIFT), a high-performance privacy-preserving SIFT feature detection system. In contrast to previous works, the proposed design is not restricted by the efficiency limitations of current homomorphic encryption scheme. In our design, we decompose and distribute the computation procedures of the original SIFT algorithm to a set of independent, co-operative cloud servers and keep the outsourced computation procedures as simple as possible to avoid utilizing a computationally expensive homomorphic encryption scheme. The proposed SecSIFT enables implementation with practical computation and communication complexity. Extensive experimental results demonstrate that SecSIFT performs comparably to original SIFT on image benchmarks while capable of preserving the privacy in an efficient way.

As today's integrated circuit (IC) has easily involved millions and even billions of gates, known as very large-scale integration (VLSI), one natural trend is to move such prohibitive in-house design procedure to the low-cost public cloud. However, such a migration is also raising a challenging request on practical and privacy-preserving techniques to safeguard the sensitive IC design data, i.e., the Intellectual Property (IP). In this paper, we initiate the first study along the direction, and present a practical system for privacy-preserving IC timing analysis, which refers to an essential and expensive procedure via repeated evaluations of timing delays on a gate-level circuit. For privacy protection, our system leverages a key observation that many IP blocks are universally reused and shared across different IC designs, and thus only a small portion of critical IP blocks need to be protected. By carefully extracting such critical design data from the whole circuit, our system only outsources the non-critical data to the public cloud. As such "data splitting" does not readily facilitate correct timing analysis, we then develop specialized algorithms to enable the public cloud to take only the non-critical data and return intermediate results. Such results can later be integrated with critical design data by the local server for fast timing analysis. We also propose a heuristic algorithm to considerably reduce the bandwidth cost in the system. Through rigorous security analysis, we show our system is resilient to IC reverse engineering and protects both the critical IP gate-level design and functionality. We evaluate our system over large IC benchmarks with up to a million of gates to show the efficiency and effectiveness.

Multimedia contents, especially videos, are being exponentially generated today. Due to the limited local storage, people are willing to store the videos at the remote cloud media center for its low cost and scalable storage. However, videos may have to be encrypted before outsourcing for privacy concerns. For practical purposes, the cloud media center should also provide the deduplication functionality to eliminate the storage and bandwidth redundancy, and adaptively disseminate videos to heterogeneous networks and different devices to ensure the quality of service. In light of the observations, we present a secure architecture enabling the encrypted cloud media center. It builds on top of latest advancements on secure deduplication and video coding techniques, with fully functional system implementations on encrypted video deduplication and adaptive video dissemination services. Specifically, to support efficient adaptive dissemination, we utilize the scalable video coding (SVC) techniques and propose a tailored layer-level secure deduplication strategy to be compatible with the internal structure of SVC. Accordingly, we adopt a structure-compatible encryption mechanism and optimize the way how encrypted SVC videos are stored for fast retrieval and efficient dissemination. We thoroughly analyze the security strength of our system design with strong video protection. Furthermore, we give a prototype implementation with encrypted end-to-end deployment on Amazon cloud platform. Extensive experiments demonstrate the practicality of our system.

Cloud storage has gained a remarkable success in recent years with an increasing number of consumers and enterprises outsourcing their data to the cloud. To assure the availability and integrity of the outsourced data, several protocols have been proposed to audit cloud storage. Despite the formally guaranteed security, the constructions employed heavy cryptographic operations as well as advanced concepts (e.g., bilinear maps over elliptic curves and digital signatures), and thus are inefficient to admit wide applicability in practice. In this paper, we design a novel secure cloud storage protocol, which is conceptually and technically simpler and significantly more efficient than previous constructions. Inspired by a classic string equality checking protocol in distributed computing, our protocol uses only basic integer arithmetic (without advanced techniques and concepts). As simple as the protocol is, it supports both randomized and deterministic auditing to fit different applications. We further extend the proposed protocol to support data dynamics, i.e., adding, deleting and modifying data, using a novel technique. As a further contribution, we find a systematic way to design secure cloud storage protocols based on verifiable computation protocols. Theoretical and experimental analyses validate the efficacy of our protocol.

In storage outsourcing, highly correlated datasets can occur commonly, where the rich information buried in correlated data can be useful for many cloud data generation/dissemination services. In light of this, we propose to enable a secure and efficient cloud-assisted image sharing architecture for mobile devices, by leveraging outsourced encrypted image datasets with privacy assurance. Different from traditional image sharing, the proposed design aims to save the transmission cost from mobile clients, by directly utilizing outsourced correlated images to reproduce the image of interest inside the cloud for immediate dissemination. While the benefits are obvious, how to leverage the encrypted image datasets makes the problem particular challenging. To tackle the problem, we first propose a secure and efficient index design that allows the mobile client to securely find from the encrypted image datasets the candidate selection pertaining to the image of interest for sharing. We then design two specialized encryption mechanisms that support the secure image reproduction inside the cloud directly from the encrypted candidate selection. We formally analyze the security strength of the design. Our experiments show that up to 90% of the transmission cost at the mobile client can be saved, while achieving all service requirements and security guarantees.

Searchable encryption is an important and challenging issue. It allows people to search on encrypted data. This is a very useful function when more and more people choose to host their data in the cloud and the cloud server is not fully trustable. Existing solutions for searchable encryption are only limited to some simple functions of search, such as boolean search or similarity search. In this paper, we propose a scheme for Generalized Pattern-matching String-search on Encrypted data (GPSE) in cloud systems. GPSE allows users to specify their search queries by using generalized wildcard-based string patterns (such as SQL-like patterns). It gives users great expressive power in specifying highly targeted search queries. In the framework of GPSE, we particularly implemented two most commonly used pattern matching search functions on encrypted data, the substring matching and the longest-prefix-first matching. We also prove that GPSE is secure under the known-plaintext model. Experiments over real data sets show that GPSE achieves high search accuracy.

Mobile advertising has become a key economic force with the growing mobile app markets. Targeted advertising has been attracting the attention of business and research communities because of its high return on investment. However, the fact that it requires continuous collection of user profiles raises severe privacy concerns. In this paper, we propose a secure and practical mobile advertising architecture, which aims to enable targeted advertising over encrypted mobile user profiles. The proposed idea is based upon a cryptographic primitive, i.e., private stream searching (PSS). We first propose a basic construction of secure targeted mobile advertising. Thorough analysis shows that our basic design achieves semantic security. After that, we introduce a number of techniques to make our design more efficient for mobile devices. Experimental results demonstrate the practicality of our proposed architecture.

Cloud storage auditing is viewed as an important service to verify the integrity of the data in public cloud. Current auditing protocols are all based on the assumption that the client's secret key for auditing is absolutely secure. However, such assumption may not always be held, due to the possibly weak sense of security and/or low security settings at the client. If such a secret key for auditing is exposed, most of the current auditing protocols would inevitably become unable to work. In this paper, we focus on this new aspect of cloud storage auditing. We investigate how to reduce the damage of the client's key exposure in cloud storage auditing, and give the first practical solution for this new problem setting. We formalize the definition and the security model of auditing protocol with key-exposure resilience and propose such a protocol. In our design, we employ the binary tree structure and the preorder traversal technique to update the secret keys for the client. We also develop a novel authenticator construction to support the forward security and the property of blockless verifiability. The security proof and the performance analysis show that our proposed protocol is secure and efficient.

Virtualization is one of the key enablers in cloud computing. At the same time, though, it is also widely considered as a double-edged sword that may cause information leakage between virtual machines (VM) co-residing on the same physical server via various cross-VM covert channels. In this paper, we first explore the impact of different bystander workloads on cross-VM covert channels. Then, we use a Continuous Time Markov Process to model the impact of bystanders on the cross-VM covert channel in terms of both the work scheduling of the virtualization platform and the intensity of the bystander workloads. Based on empirical study, we quantify the relationship between the influential factors and the transmission quality of the covert channel. A tailored and lightweight VM provisioning strategy, which aims to ensure that bystander workloads on each server can cause sufficiently high error rates to covert channels, is proposed to mitigate the threat of cross-VM covert channels while maintaining the resource efficiency of virtualization. The efficiency and efficacy of the proposed VM provisioning strategy is evaluated through trace-driven simulations.

The amount and availability of user-contributed image data have been dramatically increased during the past ten years. Popular multimedia social networks, e.g. Flicker, commonly utilize user image data to construct user behavior models, social preferences, etc., for the purpose of effective advertisement, better user retention and attraction, and many others. Existing practices of data utilization, however, seriously deteriorate users' personal privacy and have led to increasing criticisms and legislation pressures. In this paper, we aim to construct a privacy-preserving feature detection scheme over encrypted image data. The proposed system enables an interested party to perform a variety of image feature detection tasks, including visual descriptors in MPEG-7 standard, while protecting user privacy relating to image contents. We implement a prototype system based on somewhat homomorphic encryption scheme and the benchmark Caltech256 database. The experimental results show that our system can guarantee effective image feature detection without sacrificing user privacy.

The increasing popularity of images at social media sites is posing new opportunities for social discovery applications, i.e., suggesting new friends and discovering new social groups with similar interests via exploring images. To effectively handle the explosive growth of images involved in social discovery, one common trend for many emerging social media sites is to leverage the commercial public cloud as their robust backend data center. While extremely convenient, directly exposing content-rich images and the related social discovery results to the public cloud also raises new acute privacy concerns. In light of the observation, in this paper we propose a privacy-preserving social discovery service architecture based on encrypted images. As the core of such social discovery is to compare and quantify similar images, we first adopt the effective Bag-of-Words model to extract the "visual similarity content" of users' images into image profile vectors, and then model the problem as similarity retrieval of encrypted high-dimensional image profiles. To support fast and scalable similarity search over hundreds of thousands of encrypted images, we propose a secure and efficient indexing structure. The resulting design enables social media sites to obtain secure, practical, and accurate social discovery from the public cloud, without disclosing the encrypted image content. We formally prove the security and discuss further extensions on user image update and the compatibility with existing image sharing social functionalities. Extensive experiments on a large Flickr image dataset demonstrate the practical performance of the proposed design. Our qualitative social discovery results show consistency with human perception.

Wireless sensors are being increasingly used to monitor/collect information in healthcare medical systems. For resource-efficient data acquisition, one major trend today is to utilize compressive sensing, for it unifies traditional data sampling and compression. Despite the increasing popularity, how to effectively process the ever-growing healthcare data and simultaneously protect data privacy, while maintaining low overhead at sensors, remains challenging. To address the problem, we propose a privacy-aware cloud-assisted healthcare monitoring system via compressive sensing, which integrates different domain techniques with following benefits. By design, acquired sensitive data samples never leave sensors in unprotected form. Protected samples are later sent to cloud, for storage, processing, and disseminating reconstructed data to receivers. The system is privacy-assured where cloud sees neither the original samples nor underlying data. It handles well sparse and general data, and data tampered with noise. Theoretical and empirical evaluations demonstrate the system achieves privacy-assurance, efficiency, effectiveness, and resource-savings simultaneously.

As an alternative to NFC technology, 2D barcodes have been increasingly used for security-sensitive applications including payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. Due to the visual nature, 2D barcodes are subject to eavesdropping when they are displayed on the screen of a smartphone. On the other hand, the fundamental design principles of 2D barcodes make it difficult to add security features. In this paper, we propose SBVLC - a secure system for barcode-based visible light communication (VLC) between smartphones. We formally analyze the security of SBVLC based on geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen view angles and leveraging user-induced motions. We then develop two secure data exchange schemes. These schemes are useful in many security-sensitive mobile applications including private information sharing, secure device pairing, and mobile payment. SBVLC is evaluated through extensive experiments on both Android and iOS smartphones.

As the image data produced by individuals and enterprises is rapidly increasing, Scalar Invariant Feature Transform (SIFT), as a local feature detection algorithm, has been heavily employed in various areas, including object recognition, robotic mapping, etc. In this context, there is a growing need to outsource such image computation with high complexity to cloud for its economic computing resources and on-demand ubiquitous access. However, how to protect the private image data while enabling image computation becomes a major concern. To address this fundamental challenge, we study the privacy requirements in outsourcing SIFT computation and propose SecSIFT, a high performance privacy-preserving SIFT feature detection system. In previous private image computation works, one common approach is to encrypt the private image in a public key based homomorphic scheme that enables the original processing algorithms designed for plaintext domain to be performed over ciphertext domain. In contrast to these works, our system is not restricted by the efficiency limitations of homomorphic encryption scheme. The proposed system distributes the computation procedures of SIFT to a set of independent, co-operative cloud servers, and keeps the outsourced computation procedures as simple as possible to avoid utilizing homomorphic encryption scheme. Thus, it enables implementation with practical computation and communication complexity. Extensive experimental results demonstrate that SecSIFT performs comparably to original SIFT on image benchmarks while capable of preserving the privacy in an efficient way.

Short-range wireless communication technologies have been used in many security-sensitive smartphone applications and services such as contactless micro payment and device pairing. Typically, the data confidentiality of the existing short-range communication systems relies on so-called “key-exchange then encryption” mechanism, which is inefficient, especially for short communication sessions. In this work, we present${\ssb{PriWhisper}}$—a keyless secure acoustic short-range communication system for smartphones. It is designed to provide a software-based solution to secure smartphone communication without the key agreement phase.${\ssb{PriWhisper}}$adopts the emerging friendly jamming technique from radio communication for data confidentiality. The system prototype is implemented and evaluated on several Android smartphone platforms for efficiency and usability. We theoretically and experimentally analyze the security of our proposed acoustic communication system against eavesdropping. In particular, we study the (in)separability of the data signal and jamming signal against blind signal segmentation (BSS) attacks such as independent component analysis (ICA). The result shows that${\ssb{PriWhisper}}$provides sufficient security guarantees for commercial smartphone applications and yet strong compatibilities with most legacy smartphone platforms. As an application, we also develop${\ssb{AcousAuth}}$—a novel smartphone-empowered system for personal authentication.

Cloud computing offers a great opportunity to bridge the gap between the fast growing computation needs and limited local resources. However, without the adequate trust and strong integrity assurance, it would be difficult to expect clients to completely turn over control of their computation to the cloud. Hence, securing cloud computation becomes an imperative and challenging task, especially in the aspect of integrity verification. To address the challenge, we propose a hassle-free, fixed-rate, and job-based software as a service cloud model along with the integrity verification mechanisms, with particular focus on outsourcing the widely applicable engineering optimization problem, i.e., convex optimization. We aim to construct efficient integrity verification mechanisms using application-specific techniques. Our security design does not require the use of heavy cryptographic tools. Instead, we leverage the inherent structure of the optimization problems and make the computation outsourcing proof-carrying to achieve efficient integrity verification. The proposed design provides substantial computational savings on the client side and introduces marginal overhead on the cloud side. We further prove its correctness and soundness. The extensive experiments under the real cloud environment show our mechanisms ensure strong integrity assurance with high efficiency on both the client and the cloud sides and are readily applicable in current practice.

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of "coordinate matching," i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use "inner product similarity" to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication.

Cloud computing provides a “pay-per-use” utility service which offers the customer the economical access to large amount of computing resources with minimal management overhead. Despite the tremendous benefits, computation outsourcing also eliminates the customer's ultimate control over the data computation process, which makes securing cloud computation an imperative and challenging task, especially in the aspect of integrity verification. To address these challenges, in this paper we propose to research on integrity verification mechanisms for secure outsourced computations in cloud computing. In particular, we focus on outsourcing the widely applicable engineering optimization problem, i.e., convex optimization, and aim to investigate efficient integrity verification mechanisms using application-specific techniques. Our security design does not require the use of heavy cryptographic tools. Instead, we leverage the inherent structure of the optimization problems and the proof-carrying characteristics of the solving algorithms to achieve efficient integrity verification. The proposed design provides substantial computational savings on the customer side and introduce marginal overhead on the cloud side. We further prove its correctness and soundness. The extensive experiments under real cloud environment show our mechanisms ensure strong integrity assurance with high efficiency on both the customer and cloud sides and are readily applicable in practice.

Using cloud storage, users can remotely store their data and enjoy the on-demand high-quality applications and services from a shared pool of configurable computing resources, without the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in cloud computing a formidable task, especially for users with constrained computing resources. Moreover, users should be able to just use the cloud storage as if it is local, without worrying about the need to verify its integrity. Thus, enabling public auditability for cloud storage is of critical importance so that users can resort to a third-party auditor (TPA) to check the integrity of outsourced data and be worry free. To securely introduce an effective TPA, the auditing process should bring in no new vulnerabilities toward user data privacy, and introduce no additional online burden to user. In this paper, we propose a secure cloud storage system supporting privacy-preserving public auditing. We further extend our result to enable the TPA to perform audits for multiple users simultaneously and efficiently. Extensive security and performance analysis show the proposed schemes are provably secure and highly efficient. Our preliminary experiment conducted on Amazon EC2 instance further demonstrates the fast performance of the design.

Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. However, how to protect customers' confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud. Because applying traditional approaches like Gaussian elimination or LU decomposition (aka. direct method) to such large-scale LEs would be prohibitively expensive, we build the secure LE outsourcing mechanism via a completely different approach-iterative method, which is much easier to implement in practice and only demands relatively simpler matrix-vector operations. Specifically, our mechanism enables a customer to securely harness the cloud for iteratively finding successive approximations to the LE solution, while keeping both the sensitive input and output of the computation private. For robust cheating detection, we further explore the algebraic property of matrix-vector operations and propose an efficient result verification mechanism, which allows the customer to verify all answers received from previous iterative approximations in one batch with high probability. Thorough security analysis and prototype experiments on Amazon EC2 demonstrate the validity and practicality of our proposed design.

As the data produced by individuals and enterprises that need to be stored and utilized are rapidly increasing, data owners are motivated to outsource their local complex data management systems into the cloud for its great flexibility and economic savings. However, as sensitive cloud data may have to be encrypted before outsourcing, which obsoletes the traditional data utilization service based on plaintext keyword search, how to enable privacy-assured utilization mechanisms for outsourced cloud data is thus of paramount importance. Considering the large number of on-demand data users and huge amount of outsourced data files in cloud, the problem is particularly challenging, as it is extremely difficult to meet also the practical requirements of performance, system usability, and high-level user searching experiences. In this paper, we investigate the problem of secure and efficient similarity search over outsourced cloud data. Similarity search is a fundamental and powerful tool widely used in plaintext information retrieval, but has not been quite explored in the encrypted data domain. Our mechanism design first exploits a suppressing technique to build storage-efficient similarity keyword set from a given document collection, with edit distance as the similarity metric. Based on that, we then build a private trie-traverse searching index, and show it correctly achieves the defined similarity search functionality with constant search time complexity. We formally prove the privacy-preserving guarantee of the proposed mechanism under rigorous security treatment. To demonstrate the generality of our mechanism and further enrich the application spectrum, we also show our new construction naturally supports fuzzy search, a previously studied notion aiming only to tolerate typos and representation inconsistencies in the user searching input. The extensive experiments on Amazon cloud platform with real data set further demonstrate the validity and practicality of the proposed mechanism.

Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we explore the statistical measure approach, i.e., relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys “as-strong-as-possible” security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.

Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications without the burden of local hardware and software management. Though the benefits are clear, such a service is also relinquishing users' physical possession of their outsourced data, which inevitably poses new security risks toward the correctness of the data in cloud. In order to address this new problem and further achieve a secure and dependable cloud storage service, we propose in this paper a flexible distributed storage integrity auditing mechanism, utilizing the homomorphic token and distributed erasure-coded data. The proposed design allows users to audit the cloud storage with very lightweight communication and computation cost. The auditing result not only ensures strong cloud storage correctness guarantee, but also simultaneously achieves fast data error localization, i.e., the identification of misbehaving server. Considering the cloud data are dynamic in nature, the proposed design further supports secure and efficient dynamic operations on outsourced data, including block modification, deletion, and append. Analysis shows the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks.

In the emerging cloud computing paradigm, data owners become increasingly motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. For the consideration of users' privacy, sensitive data have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. In this paper, for the first time, we define and solve the problem of privacy-preserving query over encrypted graph-structured data in cloud computing (PPGQ), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Our work utilizes the principle of "filtering-and-verification". We prebuild a feature-based index to provide feature-related information about each encrypted data graph, and then choose the efficient inner product as the pruning tool to carry out the filtering procedure. To meet the challenge of supporting graph query without privacy breaches, we propose a secure inner product computation technique, and then improve it to achieve various privacy requirements under the known-background threat model.

Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. However, how to protect customers' confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud. Because applying traditional approaches like Gaussian elimination or LU decomposition (aka. direct method) to such large-scale LE problems would be prohibitively expensive, we build the secure LE outsourcing mechanism via a completely different approach -- iterative method, which is much easier to implement in practice and only demands relatively simpler matrix-vector operations. Specifically, our mechanism enables a customer to securely harness the cloud for iteratively finding successive approximations to the LE solution, while keeping both the sensitive input and output of the computation private. For robust cheating detection, we further explore the algebraic property of matrix-vector operations and propose an efficient result verification mechanism, which allows the customer to verify all answers received from previous iterative approximations in one batch with high probability. Thorough security analysis and prototype experiments on Amazon EC2 demonstrate the validity and practicality of our proposed design.

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of “coordinate matching”, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication.

Cloud computing enables customers with limited computational resources to outsource large-scale computational tasks to the cloud, where massive computational power can be easily utilized in a pay-per-use manner. However, security is the major concern that prevents the wide adoption of computation outsourcing in the cloud, especially when end-user's confidential data are processed and produced during the computation. Thus, secure outsourcing mechanisms are in great need to not only protect sensitive information by enabling computations with encrypted data, but also protect customers from malicious behaviors by validating the computation result. Such a mechanism of general secure computation outsourcing was recently shown to be feasible in theory, but to design mechanisms that are practically efficient remains a very challenging problem. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. In order to achieve practical efficiency, our mechanism design explicitly decomposes the LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computations than the general circuit representation. In particular, by formulating private data owned by the customer for LP problem as a set of matrices and vectors, we are able to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP computation and derive the necessary and sufficient conditions that correct result must satisfy. Such result verification mechanism is extremely efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design.

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of the client through the auditing of whether his data stored in the cloud are indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion, and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public auditability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for the seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the existing proof of storage models by manipulating the classic Merkle Hash Tree construction for block tag authentication. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multiuser setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis show that the proposed schemes are highly efficient and provably secure.

Ciphertext-Policy Attribute Based Encryption (CP-ABE) is a promising cryptographic primitive for fine-grained access control of shared data. In CP-ABE, each user is associated with a set of attributes and data are encrypted with access structures on attributes. A user is able to decrypt a ciphertext if and only if his attributes satisfy the ciphertext access structure. Beside this basic property, practical applications usually have other requirements. In this paper we focus on an important issue of attribute revocation which is cumbersome for CP-ABE schemes. In particular, we resolve this challenging issue by considering more practical scenarios in which semi-trustable on-line proxy servers are available. As compared to existing schemes, our proposed solution enables the authority to revoke user attributes with minimal effort. We achieve this by uniquely integrating the technique of proxy re-encryption with CP-ABE, and enable the authority to delegate most of laborious tasks to proxy servers. Formal analysis shows that our proposed scheme is provably secure against chosen ciphertext attacks. In addition, we show that our technique can also be applicable to the Key-Policy Attribute Based Encryption (KP-ABE) counterpart.

As Cloud Computing becomes prevalent, sensitive information are being increasingly centralized into the cloud. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow users to securely search over encrypted data through keywords, these techniques support only boolean search, without capturing any relevance of data files. This approach suffers from two main drawbacks when directly applied in the context of Cloud Computing. On the one hand, users, who do not necessarily have pre-knowledge of the encrypted cloud data, have to post process every retrieved file in order to find ones most matching their interest, On the other hand, invariably retrieving all files containing the queried keyword further incurs unnecessary network traffic, which is absolutely undesirable in today's pay-as-you-use cloud paradigm. In this paper, for the first time we define and solve the problem of effective yet secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by returning the matching files in a ranked order regarding to certain relevance criteria (e.g., keyword frequency), thus making one step closer towards practical deployment of privacy-preserving data hosting services in Cloud Computing. We first give a straightforward yet ideal construction of ranked keyword search under the state-of-the-art searchable symmetric encryption (SSE) security definition, and demonstrate its inefficiency. To achieve more practical performance, we then propose a definition for ranked searchable symmetric encryption, and give an efficient design by properly utilizing the existing cryptographic primitive, order-preserving symmetric encryption (OPSE). Thorough analysis shows that our proposed solution enjoys ``as-strong-as-possible" security guarantee compared to previous SSE schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.

As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users' searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing fuzzy keyword sets, which greatly reduces the storage and representation overheads. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search.

Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize and uniquely combine the public key based homomorphic authenticator with random masking to achieve the privacy-preserving public cloud data auditing system, which meets all above requirements. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.

Cloud computing is an emerging computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. As promising as it is, this paradigm also brings forth many new challenges for data security and access control when users outsource sensitive data for sharing on cloud servers, which are not within the same trusted domain as data owners. To keep sensitive user data confidential against untrusted servers, existing solutions usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, in doing so, these solutions inevitably introduce a heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired, and thus do not scale well. The problem of simultaneously achieving fine-grainedness, scalability, and data confidentiality of access control actually still remains unresolved. This paper addresses this challenging open issue by, on one hand, defining and enforcing access policies based on data attributes, and, on the other hand, allowing the data owner to delegate most of the computation tasks involved in fine-grained data access control to untrusted cloud servers without disclosing the underlying data contents. We achieve this goal by exploiting and uniquely combining techniques of attribute-based encryption (ABE), proxy re-encryption, and lazy re-encryption. Our proposed scheme also has salient properties of user access privilege confidentiality and user secret key accountability. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security models.

Cloud computing has been envisioned as the next-generation architecture of IT enterprise. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, cloud computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood. In this article, we focus on cloud data storage security, which has always been an important aspect of quality of service. To ensure the correctness of users' data in the cloud, we propose an effective and flexible distributed scheme with two salient features, opposing to its predecessors. By utilizing the homomorphic token with distributed verification of erasure-coded data, our scheme achieves the integration of storage correctness insurance and data error localization, i.e., the identification of misbehaving server (s). Unlike most prior works, the new scheme further supports secure and efficient dynamic operations on data blocks, including: data update, delete and append. Extensive security and performance analysis shows that the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks.