Graph-based vertical federated learning (GVFL) enables multiple parties to collaboratively train and infer over aligned nodes, where each party contributes its own local embedding derived from different attributes and adjacency relations. Adversarial inputs injected by an attacker can skew the joint prediction toward its desired outcomes while diminishing the influence of benign parties and undermining contribution. However, most attacks typically have pre-set assumptions, such as access to the server architecture, model queries, or in-domain auxiliary graphs. In this paper, we propose SGAC, an attack framework that enables domination of joint inference without relying on above assumptions. SGAC learns label-indicative embeddings and class-transferable probabilities to generate a surrogate that closely mimics the server-side classification behavior by exploiting auxiliary graphs from non-training domains. SGAC then leverages saliency over node attributes and edges on the auxiliary graphs to construct a diverse set of shadow inputs resembling highly influential test instances. With the surrogate fidelity and input diversity, SGAC crafts transferable contribution-monopoly adversarial inputs that hijack GVFL incentives. Extensive experiments across diverse model architectures validate SGAC's effectiveness.

Leader election serves a well-defined role in leader-based Byzantine Fault Tolerant (BFT) protocols. Existing reputation-based leader election frameworks for partially synchronous BFTs suffer from either protocol-specific proofs, narrow applicability, or unbounded recovery after network stabilization, leaving an open problem. This paper presents a novel protocol-independent abstraction formalizing generic correctness properties and effectiveness guarantees for leader election under partial synchrony, enabling protocol-independent analysis and design. Building on this, we design the Sliding Window Leader Election (SWLE) mechanism. SWLE dynamically adjusts leader nominations via consensus-behavior-based reputation scores, enforcing Byzantine-cost amplification. We demonstrate SWLE introduces minimal extra overhead to the base protocol and prove it satisfies all abstraction properties and provides superior effectiveness. We show, with a 16-server deployment across 4 different regions in northern China, SWLE achieves up to 4.2x higher throughput, 75% lower latency and 27% Byzantine leader frequency compared to the state-of-the-art solution under common Byzantine faults, while maintaining efficiency in fault-free scenarios.

Kernel operations in resource-constrained edge systems, such as memory swapping and deduplication, use the access frequency (hotness) of memory pages to guide page placement and reclamation. However, these operations suffer from page-hotness skew: a page may contain a mix of highly accessed and infrequently accessed objects, which causes inaccurate page-level classification, wasted DRAM capacity, and expensive I/O. We attribute this skewness to a cross-layer mismatch: the kernel manages memory at page granularity, whereas user-level allocators place objects without considering access hotness.

Blockchain-based electronic voting systems can achieve voter identity anonymity via cryptographic techniques such as ring signatures and blind signatures. However, fully hiding of voter information mitigates the capability of traceability. Previous mechanisms provide limited traceability, typically by preventing double-voting attacks while compromising the anonymity. From the cryptographic point, threshold signature with private accountability seems to offer a balanced solution between privacy and accountability. If directly applying it into blockchain-based electronic voting systems, it needs to fix all voters and each verification has to pre-store all voters’ public keys, incurring at least linear-size storage overhead and poor scalability. How to optimize the storage and scalability while guaranteeing both anonymity and traceability remains to be challenging. In this paper, we propose BAVote, an efficient and scalable blockchain-based electronic voting system with anonymity and traceability. It is built on top of a new threshold signature scheme named ConsATS that features a constant-size verification key. ConsATS compress all voters’ public keys into a single verification key, allowing an aggregated ballot to be verified without storing or processing per-voter public keys, thereby reducing on-chain storage overhead and improving scalability in BAVote. The aggregated signature serving as the ballot is encrypted in ConsATS, while BAVote further combines one-time addresses and a commit–reveal mechanism to protect intermediate on-chain data during voting. The corresponding tracing key enables authorized tracer to identify malicious voters during authorized audits. We implement a prototype of BAVote in both a local blockchain environment and the Ethereum Sepolia testnet. The experimental results show that the storage cost of verification keys in our system is reduced by more than 90% and the verification time is 7x faster compared to existing schemes.

Graph-based vertical federated learning (GVFL) enables an active party who owns a labeled graph to collaborate with passive parties who possess additional node features and edges to improve model performance. GVFL shares representations and gradients, allowing passive parties to retain their optimized bottom models, which makes previous GVFL algorithms unable to resist label inference attacks. However, most attacks assume that the attacker has access to the training data’s exact class space, the top model, or labeled auxiliary datasets from the training domain. These strong assumptions are not practical for real-world GVFL applications. In this paper, we propose Knowledge Transfer Attack (KTA), which leverages only auxiliary graphs from non-training domains to infer private labels. To address domain shift and ensure effective supervision transfer, KTA adapts a surrogate classifier in an aligned representation space while mitigating the negative influence of irrelevant outlier-class supervision. Specifically, KTA exploits the global consistency of cross-domain graphs and incorporates adaptive shift parameters into graph encoding. KTA then aligns cross-domain distributions within the shared class space and mitigates negative transfer by filtering outlier source classes. Experiments confirm the effectiveness of KTA in inferring the active party’s private labels and superiority over state-of-the-art attacks.

Traffic prediction is essential for modern transportation systems, enhancing traffic management and urban planning. Accurate predictions of traffic flow and speed are crucial for understanding road usage, mitigating congestion, and providing real-time traffic monitoring and dynamic route guidance, thus improving road safety and infrastructure efficiency. Traditional research has often focused on predicting traffic flow or speed independently, leading to higher resource consumption due to the need for separate models. Few studies have explored the simultaneous prediction of both metrics, with recent attempts failing to account for spatial correlations, resulting in suboptimal performance. To address these challenges, we propose MTNet, a multi-task learning framework for joint traffic flow and speed prediction. MTNet employs a Transformer-like Encoder-Decoder architecture to process and enhance feature representations, capturing complex spatio-temporal correlations. Specifically, MTNet extracts intra-task dependencies using a cross-task interaction module and models task-specific spatiotemporal dependencies using spatial and temporal-aware modules with cascaded residual structures. Additionally, spatio-temporal positional encoding is integrated to increase awareness of long-term and long-distance dependencies. Extensive experiments on three diverse traffic datasets-Manchester, PeMSD4, and PeMSD8-demonstrate that MTNet significantly outperforms state-of-the-art methods in both traffic flow and speed prediction. MTNet achieves substantial improvements in prediction accuracy and efficiency, striking an optimal balance between performance and computational resource usage.

Optimizing memory cost-efficiency is the top demand for many cloud computing scenarios. Memory deduplication and hugepage are both essential techniques for reducing memory cost and improving efficiency. However, the simultaneous use of memory deduplication and hugepages faces a dillema. Existing approaches either split hugepages into small pages to achieve efficient memory deduplication or ignore redundant portions within hugepages to maintain hugepage performance.

System auditing frameworks are crucial for modern data center security, as they record system events to detect intrusions. However, existing software-based auditing frameworks are limited by their high runtime overhead. To address the limitations of software-based frameworks, researchers had proposed a hardware-based auditing framework that offloads log processing to isolated hardware. However, despite using powerful specialized hardware, this approach still suffers from high runtime overhead, which contradicts their efficiency goal. We have identified that the high overhead is due to the pushbased architecture, which involves operating a log sender on the monitored host. Consequently, the existing approach requires heavy software protection mechanisms to secure the log sender, resulting in high runtime overhead.In this paper, we propose a new DPU-assisted pull-based architecture called DPUaudit for hardware-based auditing, which achieves near-zero runtime overhead. Instead of using a log sender, DPUaudit utilizes DPU to actively pull system events from the monitored host. This eliminates the need for heavy mechanisms to handle and safeguard the log sender, achieving highly efficient system auditing. Experimental results show that, on average, DPUaudit only slows down applications on the monitored host by 2.1% for six mainstream data center applications under different workloads, which is at least one order of magnitude smaller than existing approaches, while still ensuring the integrity of audit logs.

Vertical federated learning (VFL) enables feature-level collaboration by incorporating scattered attributes from aligned samples, and allows each party to contribute its personalized input to joint training and inference. The injection of adversarial inputs can mislead the joint inference towards the attacker’s will, forcing other benign parties to make negligible contributions and losing rewards regarding the importance of their contributions. However, most attacks require server model queries, subsets of complete test samples, or labeled auxiliary images from the training domain. These extra requirements are not practical for real-world VFL applications. In this paper, we propose PGAC, a novel and practical attack framework for crafting adversarial inputs to dominate joint inference, which does not rely on the above requirements. PGAC advances prior attacks by requiring only access to auxiliary images from non-training domains. PGAC learns generalized label-indicative embeddings and estimates class-transferable probabilities across domains to generate a proxy model that closely approximates the server model. PGAC then augments images by emphasizing salient regions with class activation maps, creating a diverse shadow input set that resembles influential test inputs. With proxy fidelity and input diversity, PGAC crafts transferable adversarial inputs. Evaluation on diverse model architectures confirms the effectiveness of PGAC.

In wireless communication, data is typically encrypted before transmission to ensure confidentiality, which complicates the implementation of access control.Predicate encryption based (PE-based) access control enables fine-grained and secure control over encrypted data. The previous PE-based access control systems only guarantee the access of the authorized receivers but not control illegal senders, and have to execute re-authorization due to its static setting. In this paper, we propose a verifiable predicate-based access control encryption supporting dynamic revocation scheme (dvPACE), which is a dynamic access control mechanism on both senders and receivers. In contrast to existing access control, dvPACE controls not only "who can access" but also "who can send". It involves an additional sanitizer to handle the cipher data and embraces multi-verification against illegal entities. To achieve efficiency and scalability, dvPACE embeds inner-product predicate encryption and borrows the idea of identity-based revocation systems. dvPACE achieves about 19ms computational cost of data access, a comparable performance over conventional mechanisms with improvements of functionalities.

System auditing frameworks are essential for operating system security as they record system events to support intrusion detection, compliance verification and attack reconstruction. However, existing auditing frameworks fail to meet the stringent requirements of real-time systems, which demand security, predictability, and efficiency. Though current solutions are optimized for security or performance, they do not focus on bounding the worst-case execution time (WCET) and incorporating into response-time analysis (RTA). This paper presents RT-NODROP, a secure and predictable auditing framework tailored for real-time systems. RT-NODROP employs a lightweight threadlet-based architecture to isolate audit events processing, periodically invoking threadlets to simultaneously bound WCET and event residence time. By integrating with real-time schedule, RT-NODROP ensures no event dropping, system efficiency, and predictability. We further develop an overhead-aware RTA and a period selection algorithm to balance security, performance, and schedulability. The evaluations demonstrate that RT-NODROP is superior over state-of-the-art frameworks (Sysdig, OMNILOG, Ellipsis), improving schedulability by$\mathbf{8 0. 1 1 \%, ~} \mathbf{1 1 7. 9 \%}$and$\mathbf{5 1. 0 5 \%}$, respectively. For the latency-intensive application Redis, RT-NODROP achieves up to$\mathbf{7 5. 1 \%}(\mathbf{1 3 8. 8 6 \%}, \mathbf{3 2 4. 6 \%})$higher throughput and$\mathbf{2. 1 9} \times$(3.07x, 5.02x) lower 99.9th percentile tail latency than Sysdig (OMNILOG, Ellipsis) while maintaining a minimum event residence time around 10 ms without event dropping.

The rapid development of the Internet of Things has increased the importance of IoT data privacy. Traditional encryption and access control mechanisms are insufficient for ensuring privacy. Blockchain-based covert communication offers enhanced concealment, anonymity, and immutability for secure information exchange over open networks. However, existing blockchain-based schemes face limitations in point-to-point communication and low screening efficiency and concealment, as well as challenges when extended to group scenarios, such as the existence of leakers. To address these issues, we propose a Blockchain-based Group Covert Communication (BGCC) scheme. BGCC leverages broadcast encryption to revoke leakers and introduces an efficient covert filtering mechanism based on the decisional ℓ-BDHE assumption. We prove its concealment through security reduction, statistical tests, and machine learning test. Experimental results demonstrate that BGCC outperforms existing schemes.

Although consortium blockchain has an identification mechanism, the captured internal clients are potentially threatening internal blockchain nodes. Internal Distributed Denial-of-Service (DDoS) attacks threaten the specific nodes in consortium blockchain, e.g., the executor, consensus, and committer nodes. Typical attack methods may include SYN Flooding and ACK Flooding and deny normal transaction service by sending many invalid transactions and blocks. In this work, we have proposed an organization collaboration-based DDoS defense approach and a Deep Q-learning (DQN)-based Moving Target Defense (MTD) for changing attack surface of victims in consortium blockchain. On one hand, contracts are used to synchronize attack information obtained from organizations, e.g., bots’ IP addresses and public keys. On the other hand, we have developed a DQN-based MTD defense mechanism for organizations to change the attack surface of victims in order to mitigate the malicious traffic, in the case of missing detections of bots. Our approach applies a multi-stage game to reflect interactions between attackers and defenders. The evaluation results have demonstrated that our approach could effectively mitigate DDoS attacks in consortium blockchain.

Companies such as CrowdStrike now offer cloud-based services for provenance analysis, which collects low-level system events from a customer's device and compiles them onto a centralized platform to detect APT attacks. Despite the effectiveness of such solutions, their privacy implications remain unclear. To assess the privacy implications of system provenance analysis, we employ the Website Fingerprinting (WF) of The Onion Router (Tor) browsers as the real-world attack scenario. In contrast to conventional, network traffic-based WF techniques, we have designed ProvAudit, a fully automated solution that audits the web browsing history of Tor browsers based on system provenance data. We conduct the first systematic case study to demonstrate the feasibility of inferring the websites visited by Tor browsers solely based on the collected system provenance data, particularly system call traces. The evaluation results show that our approach achieves a precision of 0.74 in the open-world scenario, higher than the state-of-the-art robust WF technique. In practice, ProvAudit consumes approximately 23 MB of memory and 4% CPU to audit system provenance data. Our approach is more robust against simple adversarial methods, more accurate, and less expensive than existing solutions. Overall, our case study reveals that provenance data is susceptible to privacy breaches, potentially exposing more high-level information than anticipated.

With the development of information and communication technology, the Internet of Things (IoT) has gained significant popularity across various applications. As the number of devices surges and data generation accelerates, robust data security and accurate data retrieval have become increasingly important. Fuzzy keyword search provides an elegant way to allow retrieval over encrypted data. Privacy and accuracy are two important factors when applying fuzzy keyword search into cloud storage systems. State-of-the-art mechanisms cannot effectively balance privacy and accuracy, and they even compromise the access pattern. In this paper, we propose SeaPA, a fuzzy keyword search scheme with strong privacy and high accuracy. SeaPA is built on top of two non-colluding servers and integrated with secure multi-party computation. To construct SeaPA, we introduce a top-k document retrieval algorithm that conceals the similarity of search results from identical queries, preventing access pattern leakage. We implement a prototype system and conduct extensive experiments on the 20NewsGroups dataset. The results show that SeaPA achieves higher accuracy, with over$16\times$speedups in index encryption efficiency and over$9.3\times$speedups in search efficiency, compared to previous schemes.

Vertical Federated Learning(VFL) has gained widespread attention due to its ability of enabling collaborative model training among participants with diverse data features.Differential Privacy(DP) offers provable privacy guarantees for VFL, but existing DP-based methods typically compromise accuracy for privacy protection. To address this issue, we propose a novel scheme, called Adaptive Differential Privacy-based Vertical Federated Learning (Ada-VFed), that enhances privacy of data features and labels by adding Gaussian noise separately to the transmitted intermediate results and gradients. To improve model accuracy, we incorporate adaptive constraints through regularization terms in the objective function to mitigate the impact of clipping operations. In addition, we propose a dynamic noise injection mechanism that adjusts noise according to the importance of each dimension, thereby balancing privacy protection and model accuracy. Our theoretical analysis provides privacy guarantees and convergence insights. Extensive experiments demonstrated that our scheme significantly outperforms state-of-the-art DP-based VFL methods in terms of accuracy. Even with a small privacy budget (e.g.,ϵ = 0.5), our method improves the accuracy on MNIST, FashionMNIST, and CIFAR-10 by 13.01%, 10.08%, and 3.40%, respectively, compared to traditional DP-based VFL methods.

This paper introduces the Deferred Byzantine Generals Problem, a variant of the Byzantine Generals Problem which focuses on ensuring replicas maintain consistency over timed-release secret operations (operations that can only be known after a specified time or event). The solution to the problem is called the Deferred Byzantine Fault Tolerant (DBFT) consensus. DBFT can operate exclusive or be interleave with BFTs to handle specific tasks at designated sequence numbers or views, thereby facilitating the implementation of certain system-desirable features or supporting novel applications. It does not rely on existing timed-release primitives, but instead ensures its timed-release property through voting interactions. We presents the system model of DBFT SMR under partial synchronization using Threshold Public Key Encryption (TPKE) as the cryptographic primitives, highlighting the core issues. Then we design and implement the DBFT protocol using PBFT notations, focusing on the unique parts to facilitate expansions to other paradigms. Through experimental results, we show the impact of different executing modes and parameter choices on performance and discuss potential optimizations.

The widespread developments of blockchain bring about diverse blockchain networks, where each stands as an isolated data island and operates independently. The need of interoperability and interconnection among kinds of blockchains inspires the emergence of the cross-chain technology. It enables the asset transfer and information interaction via crosschain transactions. The previous cross-chain transaction systems are almost implemented over the plain data, fully exposing the transaction-associated information. Cryptographic techniques such as non-interactive zero-knowledge proof can be used to protect the transaction privacy, while its high complexity incurs heavy running cost. The privacy of cross-chain transactions is still a challenging issue. In this paper, we propose a lightweight crosschain transaction privacy-preserving method named PTCross. It embeds Bulletproofs and Pedersen commitment to hide crosschain transaction information, meanwhile combining off-chain computation and on-chain contract verification. We instantiate and implement PTCross between ChainMaker and Bitcoin. The experimental results show that the proposed PTCross achieves both lightweight performance and strong privacy.

Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose GBSF, a generic framework for blockchain-based steganography. The sender generates the required fields, where the additional covert data is embedded to enhance the channel capacity. Based on GBSF, we design R-GAN that utilizes the generative adversarial network (GAN) with a reversible generator to generate the required fields and encode additional covert data into the input noise of the reversible generator. We then explore the performance flaw of R-GAN and introduce CCR-GAN as an improvement. CCR-GAN employs a counter-intuitive data preprocessing mechanism to reduce decoding errors in covert data. It incurs gradient explosion for model convergence and we design a custom activation function. We conduct experiments using the transaction amount of the Bitcoin mainnet as the required field. The results demonstrate that R-GAN and CCR-GAN allow to embed 11-bit (embedding rate of 17.2%) and 24-bit (embedding rate of 37.5%) covert data within a transaction amount, and enhance the channel capacity of state-of-the-art works by 4.30% to 91.67% and 9.38% to 200.00%, respectively.

Malicious websites are considered one of the top threats to the modern Internet. Thus, it is critical to effectively detect malicious websites for the security of the Internet. Conventional technologies typically rely on URL blacklists, or static and dynamic code analysis, which are known to have limitations. In order to effectively detect malicious websites, in this paper, we study malicious websites from the perspective of system provenance analysis for the first time. We first conduct a systematic feature engineering study on thousands of benign and malicious websites from the perspective of system provenance data. In our study, we discover eight useful features for malicious website detection. Based on these eight features, we propose ProvWeb, a novel non-intrusive system provenance-based tool, for malicious website detection. In our evaluation, ProvWeb can achieve an F1 score of 93.7% ∼ 99.7% for the four combinations of browsers and OSes (Windows Chrome, Windows Firefox, Linux Chrome, Linux Firefox). This result confirms that the features discovered in provenance graphs are effective in detecting malicious websites.

Blockchain-based censorship-resistant systems enable the user to access the blocked content through a covert channel while avoiding a suspicious network connection between the user and the proxy. However, state-of-the-art blockchain-based censorship-resistant schemes cannot satisfy both low communication fees and unobservability, and their method of identifying transactions with covert data may inadvertently expose the covert channel. In this paper, we present Hades, a blockchain-based covert channel framework that aims to circumvent censorship. Hades allows users to encode covert data as a transaction field, and identify transactions with covert data by using another transaction field as a label. We also present the security model for Hades, which defines the unobservability of Hades as the indistinguishability of transactions with covert data from normal transactions. We further propose two cost-friendly and unobservable instantiations of Hades: the basic RDSAC and the improved DDSAC. RDSAC uses private keys to encode covert data and utilizes random factors in the signing process as labels, while incurring a communication delay. DDSAC avoids the delay by encoding covert data into random factors and sampling a transaction amount from normal transactions as the label. We implement a prototype system of Hades and evaluate its performance. Experiment results show that our Hades prototype is unobservable, robust, and efficient. RDSAC and DDSAC can identify 1,654 transactions in 6.054 seconds and 0.071 seconds, respectively. Hades supports 1KB data transfer at $0.44 on the Bitcoin mainnet and cost-free data transfer on the Bitcoin testnet.

Covert channels in blockchain networks achieve undetectable and reliable communication, while transactions incorporating secret data are perpetually stored on the chain, thereby leaving the secret data continuously susceptible to extraction. MTMM (IEEE Transactions on Computers 2023) is a state-of-the-art blockchain-based covert channel. It utilizes Bitcoin network traffic that will not be recorded on the chain to embed data, thus mitigating the above issues. However, we identify a distinctive pattern in MTMM, based on which we propose a comparison attack to accurately detect MTMM traffic. To defend against the attack, we present an improvement named ORIM, which exploits the permutation of transaction hashes within inventory messages to transmit secret data. ORIM leverages a pseudo-random function to obscure the transaction hashes involved in the permutation to ensure unobservability. The obfuscated values, rather than the original transaction hashes, are utilized to encode the confidential data. Furthermore, we introduce a variable-length encoding scheme predicated on complete binary trees. This scheme considerably amplifies the bandwidth and facilitates efficient encoding and decoding of secret data. Experimental results indicate that ORIM maintains unobservability and that ORIM’s bandwidth is approximately$3.7\times $of MTMM.

Cryptographic access control guarantees that authorized users can access data while unauthorized get nothing. Such an all-or-nothing access mode achieves secrecy but does not fit strong-privacy scenarios. FE-based access control breaks it and reaches a balance between data privacy and data utilization. To resist malicious senders, Damgard et al. introduced sanitizable functional encryption that enables a bi-directional control to both senders and receivers. However, its centralized structure means that the compromise of the authority incurs massive secret leakage and undermines the system’s reliability. In this work, we present SanIdea, a sanitizable, decentralized and privacy-preserving access control framework which embraces a sanitizer in the distributed-authority-domain access control setting. We instantiate it by proposing a cryptographic primitive named sMABE, which adds a$\mathsf {Sanitize}$algorithm over multi-authority attribute-based encryption. We formally prove its security in the IND-CPA model and the Sanitization Security model under the DBDH assumption. We demonstrate its reasonable efficiency through algorithm simulation, where the sanitization time is less than 0.1s with the configuration of 5 attribute authorities and 25 user attributes. We design an SABC system by integrating SanIdea with the blockchain, where SABC uses a smart contract to ensure the correctness of the distributed secret key parts. We implement SABC in an Ethereum testbed and the experiment results show that the$\mathsf {upload}$algorithm costs about 163000 user gas and the$\mathsf {download}$algorithm costs about 84000 user gas, which is cost-reasonable.

Access control encryption enables access control on both senders and receivers, and enhances message sanitization compared with traditionally cryptographic access control mechanisms. However, it is usually built on top of encrypted messages, which makes it difficult to identify malicious data and amplifies abusive message transmission. The message franking and source tracing mechanisms facilitate a report of abusive messages while support only plain-data moderation in end-to-end encryption. In this work, we present sMAC, a sanitizable and moderate access control framework which supports both sanitization and moderation over the encrypted messages as well as the data privacy, sender anonymity and backward security. We instantiate it by proposing a cryptographic primitive named amenable ACE, which expands the message accountability algorithm module in addition to access control encryption. We give formal security proof of amenable ACE in the standard model. The experimental results show that amenable ACE is efficient where the computational cost of Decrypt, Stamp, Verify and Inspect is independent of the message size.

Cross-chain systems provide a way for isolated blockchains to communicate and exchange assets and data with each other. Sidechain-based cross-chain systems support more scenarios with more complicated functionalities. However, the correlation of transactions in two underlying blockchains makes the linkability for the sidechain and exposes the identity of transaction parties and transaction amounts. This incurs the cross-chain privacy leakage. Traditional privacy-preserving mechanisms conversely compromise the regulation of transactions, which limits the identification and punishment to malicious transaction parties. To balance privacy and regulation of cross-chain systems, in this paper, we propose PCP, a privacy-preserving policy-enforcement cross-chain protocol between Monero and Bitcoin. It leverages the signature of knowledge to guarantee the correctness and privacy, while sets a trapdoor for tracing authority to revoke the anonymity when the investigation is required. We instantiate a scheme with formal security proof. We conduct a series of experiments by using Fiat-Shamir paradigm with zero-knowledge and the results show that the proposed PCP is cost-reasonable with constant 150 ms for Swap Monero phase, 40 ms for proof generation and 24 ms for proof verification.

Internet of Medical Things (IoMT) technology has gained a lot of attention. In the IoMT system, IoT devices collect and upload patient data through sensors, and doctors treat them remotely. In this process, doctors need to verify the correctness of the patient’s body data in bulk, a function that requires aggregated signature technology to achieve. However, due to the limitation that traditional verification of aggregated signatures requires access to all messages, doctors cannot efficiently verify the correctness of a particular piece of data. This will result in a significant additional verification overhead. Therefore, the efficiency problem of data batch authentication remains unsolved but imperative. In this paper, we propose an identity-based locally verifiable aggregated signature, (ID-LVEAS), that enables efficient local verification of patient data in IoMT. Building on top of both hybrid hash and inverse framework, we instantiate an ID-LVEAS scheme and prove its security in the EU-CMA security model. Based on ID-LVEAS, we further propose ID-based locally verifiable signcryption (ID-LVASC) that supports both confidentiality and integrity. We also present an instantiation and an application into IoMT from the proposed ID-LVASC. We conduct extensive experiments on the ID-LVEAS and ID-LVASC design. The results show that both cost constantly 0.01s for the verification, independent of the number of messages, which means our research has great promise for application in IoMT.

As graph data becomes increasingly prevalent in mobile computing scenarios, deploying Graph Convolutional Network-based self-supervised learning (GCN-SSL) models on mobile devices provides a powerful solution for analyzing graph data and enhancing the intelligence of various mobile services. However, ensuring the legitimacy and security of these models is crucial to protect against compromised or unauthorized versions that could lead to security vulnerabilities or intellectual property issues. In this work, we propose PoGSSL, a verifiable proof of GCN-SSL model training that authenticates model integrity and provenance by checking the reproducibility of the specific model training process. We then introduce SecVerGSSL from PoGSSL to provide privacy-preserving verified computation services. SecVerGSSL offloads the entire computation to the cloud and equips servers with customized secure components, enabling effective verified computation over secret-sharing encrypted training data and PoGSSL. Extensive experiments demonstrate that SecVerGSSL offers verification accuracy indistinguishable from plaintext results, with overhead on the verifier-side requiring at most 10.35 milliseconds and 65.98 KB per epoch.

Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future Advanced Persistent Threats (APT) defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry (1) overlooking client-side overhead; (2) imbalancedalarm triage cost and interpretation cost; and (3) excessive server side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry.

Memory corruption vulnerabilities can have more serious consequences in WebAssembly than in native applications. Therefore, we present \tool, the first WebAssembly runtime with memory isolation. Our insight is to use MPK hardware for efficient memory protection in WebAssembly. However, MPK and WebAssembly have different memory models: MPK protects virtual memory pages, while WebAssembly uses linear memory that has no pages. Mapping MPK APIs to WebAssembly causes memory bloating and low running efficiency. To solve this, we propose \acfdilm, which protects linear memory at function-level granularity. We implemented \acdilm into the official WebAssembly runtime to build \tool. Our evaluation shows that \tool can prevent memory corruption in real projects with a 1.77% average overhead and negligible memory cost.

In recent years, Internet of Things (IoT) technology has gained a lot of attention. With the development of IoT technology, it comes the need for devices with different trust domains to interact and collaborate. In order to protect the security and reliability of the communication between devices in different trust domains, it raises the concerns about the technology of cross-domain authentication. Traditional cross-domain authentication methods may lead to heavy key management overhead or depend on trusted third parties, while existing blockchain-based cross-domain authentication schemes do not prevent the possibility of mischief by malicious domain managers. In this article, we design an efficient Blockchain and trusted execution environment (TEE)-assisted secure device authentication scheme for cross-domain IoT system, called blockchain and TEE-assisted authentication. Our solution solves the problem that managers are not fully trusted, which in turns protect the security and reliability in the blockchain-based cross-domain communication. Specifically, blockchain is introduced to build trust between different domains, the identity-based signatures are used to verify the identity information of devices and the TEE is introduced to prevent the possibility of mischief by domain managers. Finally, our experiments show that the introduction of TEE has greatly improved system security with a low-efficiency reduction, which proves that our scheme can achieve highly secure distributed IoT authentication.

Federated learning (FL) models are vulnerable to membership inference attacks (MIAs), and the requirement of individual privacy motivates the protection of subjects where the individual data is distributed across multiple users in the cross-silo FL setting. In this paper, we propose a subject-level membership inference attack based on data augmentation and model discrepancy. It can effectively infer whether the data distribution of the target subject has been sampled and used for training by specific federated users, even if other users (also) may sample from the same subject and use it as part of their training set. Specifically, the adversary uses a generative adversarial network (GAN) to perform data augmentation on a small amount of priori federation-associated information known in advance. Subsequently, the adversary aggregates two different outputs from the global and tested user models using an optimal feature construction method. We simulate a controlled federation configuration and conduct extensive experiments on real datasets that include both image and categorical data. Results show that the area under the curve (AUC) is improved by 12.6% to 16.8% compared to the classical membership inference attack. This is at the expense of the test accuracy of the data augmented with GAN, which is at most 3.5% lower than the real test data. We also explore the degree of privacy leakage between overfitted models and well-generalized models in the cross-silo FL setting and conclude experimentally that the former is more likely to leak individual privacy with a subject-level degradation rate of up to 0.43. Finally, we present two possible defense mechanisms to attenuate this newly discovered privacy risk.

Ethereum leverages ECDSA as the digital signature scheme to validate transactions. From the provable security standpoint, ECDSA built on an 80-bit security Elliptic Curve group can achieve at most 50-bit concrete security, rather than 80-bit security, due to its reduction loss for$2^{30}$signature queries in security analysis. The state-of-the-art ECDSA scheme comes with no de facto formal security guarantee. Although there have been many signatures with higher concrete security, their structures are quite different from ECDSA and a total replacement of the signature field in Ethereum will incur high deployment cost. In this work, we present EthereumX without compromising the signature structure in Ethereum while achieves better security. The security gain is built on top of a new technique named randomness preprocessing module (RPM), which can securely pre-generate and verify randomness with the help of Ethereum. Calling RPM allows to pre-select randomness, which will be used for the subsequent signature, and to verify the randomness, assuring that it is previously generated. We give an instantiation with formal security guarantee and prove that it can be improved to 80-bit concrete security under the same discrete logarithm assumption as ECDSA. From this instantiated scheme, we implement EthereumX via a deployment into a locally simulated network. Experiment results show that EthereumX costs 5 seconds for a block generation which is equal to Ethereum, and generates/verifies at least$17017/10623$transactions per second that is practical enough in application, even if they are slightly slower than Ethereum which generates/verifies at least$17908/11257$transactions per second. We also mention that RMP can be applied to other DL-based signatures for the security improvement.

The recent COVID-19 pandemic has highlighted the importance of food safety and supply chain governance. In other words, we need to ensure traceability along the supply chain and support high-frequency transactions, effective data collections, etc. Thus, we posit the potential of using a lightning network, which is a decentralized traceable paradigm for achieving high-frequency transactions in blockchain-based systems. In addition, we also utilize edge computing to help facilitate data collection. However, a key challenge in securing food supplies is determining the optimal global transaction path in the lightning network while achieving efficiency and meeting the dynamic nature of food supply management. Thus, we propose a blockchain-edge scheme that utilizes our proposed dynamic programming to produce optimal solutions for selecting global transaction paths. Specifically, our scheme optimizes routing fees under existing constraints (e.g., transmission cost, computing resource consumption, and lightning network balance). The findings from our evaluations demonstrate the utility of our proposed approach in facilitating food safety management.

Electronic patient record (EPR) gains popularity in the health care system with easy availability and usually employs searchable encryption to guarantee both record confidentiality and record retrieval. However, traditional searchable encryption schemes cost a linear complexity of database and even reveals the query index. Both problems hinder EPR applications thereby balancing privacy and search efficiency becomes challenging. In this article, we design a cryptographically searchable EPR system named FastPRS which supports strong privacy and high performance. FastPRS is built on top of online/offline puncturable search (OOPS), a newly defined primitive which splits the search process into the offline phase and the online phase and embeds an index in puncturable pseudorandom sets. Leveraging a combination of linearly homomorphic encryption and oblivious transfer, the query index is punctured. OOPS, therefore, hides the query index obliviously and reduces the search complexity to a sublinear size. We also implement a FastPRS prototype system that supports more than 60 000 keywords and evaluation results show that FastPRS optimizes the search efficiency to 417 search operations per millisecond.

The concept of time release provides a new mode of sending information to the future, where the message will be available after a certainly specified period. Time-release encryption (TRE), as a promising approach, has a widespread releasing application and ensures data confidentiality. TRE relies on two main frameworks, one based on the time-lock puzzle and the other based on a trusted third party, while this primitive is impeded by frameworks’ unreliability limitations and time disclosure. We present ReleaseSC, a decentralized and privacy-aware system that combines smart contract with TRE. ReleaseSC leverages a novel three-part architecture that harmonizes time release and contracts, enabling efficient time-hidden smart contract. Our prototype is with reasonable performance through EVM evaluations on both private chain and official test network. ReleaseSC is built on the top of a new cryptographic notion named ID-based TRE that supports a flexible time policy and preserves the to-be-released time. We propose an ID-based TRE instantiation and show rigorous security analysis in formal security models. The insight from ReleaseSC will open more possibilities to address security issues in hybridized cryptography-blockchain systems.

Business organization performs its business activities within a headquarter-branch relationship, especially for multinationals, by establishing work places in accordance with business requirements. The data-driven decision making stimulates the importance of data analysis. Machine learning (ML), as a method of data analysis, automates analytical model building based on the idea that systems can train and learn from data to make decisions with minimal human intervention. For high accuracy, traditional ML algorithms make design tradeoffs, conceding privacy and reliability, and are thereby unable to satisfy strong security demands. To resolve design tensions, in this article, we propose RePEL, which harmonizes functional encryption (FE) and blockchain on top of encrypted learning. RePEL allows the headquarter to only share partial information about business data collected from branches while manages data transfer with the consensus mechanism in the blockchain, which works in a coordinated way for preserving conditional privacy and high reliability. We instantiate a RePEL design with the three-layer framework and formally reduce its security to a provably secure FE scheme. We further deploy Feel_BC to implement a RePEL prototype system, so as to realize the performance evaluation. Experimental results show that RePEL, with the basic premise of privacy and reliability, can achieve high accuracy and reasonable throughputs.

Cloud computing is considered as one of the most prominent paradigms in the information technology industry, since it can significantly reduce the costs of hardware and software resources in computing infrastructure. This convenience has enabled corporations to efficiently use the cloud storage as a mechanism to share data among their employees. At the first sight, by merely storing the shared data as plaintext in the cloud storage and protect them using an appropriate access control would be a nice solution. This is assuming that the cloud is fully trusted for not leaking any information, which is impractical as the cloud is owned by a third party. Therefore, encryption is mandatory, and the shared data will need to be stored as a ciphertext using an appropriate access control. However, in practice, some of these employees may be malicious and may want to deviate from the required sharing policy. The existing protection in the literature has been explored to allow only legitimate recipients to decrypt the contents stored in the cloud storage, but unfortunately,no existing workdeals with issues raised due to the presence of malicious data publishers. Malicious data publishers construct data following the given policy, but the ciphertexts can actually be decrypted by unauthorized users without valid keys, or simply, anyone else who is unauthorized. The impact of the involvement of malicious data publishers is detrimental, as it may damage intellectual properties from the corporations. Therefore, it remains an elusive research problem on how to enable a sound approach to resolve the issue when malicious data publishers are involved in the system, which is a very practical question. In this work, we presenta new direction of researchthat can cope with the presence of malicious data publishers. We resolve the aforementioned problem by proposing the notion of Sanitizable Access Control System (SACS), which is designed for a secure cloud storage that can also resist against malicious data publishers. We define the threat model and its formal security model, as well as its design and scheme which is based on$q$q-Parallel Bilinear Diffie-Hellman Exponent Assumption. We provide the security proof of our construction as well as its performance analysis. We believe that this work has opened a new area of research which has never been explored before, even though it is very practical. Therefore, this work will enhance the adoption of secure cloud storage in practice.

Encrypted retrieval ensures the secure retrieval over the encrypted data without sacrificing the confidentiality. Its applications in the database systems have brought this primitive under the spotlight. Once the malicious sender sends the wrong message, conventional encrypted retrieval is arduous to provide message-moderation and even abuse reporting arises when any receiver is allowed to fabricate and convince a malicious message. We introduce the idea of searchable message-moderation, a newly exquisite framework named CleanSE that enables the system to simultaneously provide encrypted retrieval and fallacious reporting resistance. In a nutshell, CleanSE is achieved via a securely technical combination of asymmetric message franking and searchable encryption. We design two CleanSE schemes. The first, calledRecon, leverages designated verifier signatures to generate a report proof such as to prevent fallacious report and undeniability, and assists with designated-server searchable encryption to protect confidentiality and privacy. Our improved scheme is calledReclean. Compared withRecon,Recleanadds an additional algorithm,$\mathsf {Clean}$, to filter forged message and employs asymmetric message franking to provide stronger deniability and accountability. We implement and evaluate prototype of our CleanSE system to highlight its feasibility and practicality.

Broadcast encryption provides a promising technique of data access control for specified users in cloud computing. A data uploader can generate a ciphertext for a set of chosen users such that only the intended users are able to access the data. However, with the rapidly increasing of collaboration between users, it is desired to extend the receiver set to grant decryption right for more users. The existing broadcast encryption systems cannot support receiver extension. In this article, we for the first time take this problem into consideration and give a solution. We take the merits of identity-based cryptosystem and propose a notion of EIBBE: a flexible data access control with receiver extendable for cloud computing based on broadcast encryption. It allows the authorized user to extend the receiver set$S$stated in the IBBE ciphertext by adding a new receiver set$S^{\prime }$without re-encryption. Both the users in$S$and$S^{\prime }$can access the data successfully. Moreover, the data uploader determines the maximum number of extended receivers. We then give a concrete construction of EIBBE and provide a rigorous security analysis of our proposed scheme. Finally, we demonstrate the scheme's efficiency and feasibility.

Internet of Things (IoT) makes great development and gains popularity with a mature combination with cloud computing. Plaintext data can be encrypted when data owners try to secure the confidentiality, while the encrypted data retrieval can be achieved by a cryptographic primitive named searchable encryption. Public-key encryption with keyword search (PEKS) is built on the asymmetric setting, however, the “honest-but-curious” assumption in PEKS creates challenges on the search reliability when malicious behaviors happen. It is nontrivial to enable the reliable search while preserving keyword privacy, as users who have paid need to receive either correct-and-wanted results or compensations. In this work, we apply blockchain to resolve this problem and design SearchBC, a blockchain-based PEKS framework supporting private, reliable, and fair encrypted search over the asymmetric setting. SearchBC is built on top of the blockchain and a keyword server to allow fairness in transactions and keyword preprocessing. We present a SearchBC instantiation and formally prove its security under the newly defined security model. SearchBC guarantees that search operations are fair and reliable and that the used keyword keeps privacy. The implementation results show that SearchBC provides a feasible means with reasonable communication and computation costs.

A procurement protocol is a protocol for a buyer to purchase digital goods at their prices from a vendor. A procurement protocol with privacy preservation can be achieved by priced oblivious transfer (POT). POT allows the buyer to obliviously procure items one by one. An adaptive POT protocol only consumes O(1) communication cost in each transaction, where all items are committed and encrypted before transactions. However, we found that the state-of-the-art adaptive POT protocol proposed by Rial et al. is less practical and does not meet real-world needs. It has to restrict to the one-buyer setting where all items are encrypted associated with one buyer's public key. For multiple buyers, the vendor must respectively encrypt all the same items for each buyer. Besides, it has to employ computationally expensive primitives such as zero-knowledge proof which imply inefficient computation operations. It is therefore unscalable and unsuitable in large-scale applications. In this paper, we propose an efficient adaptive priced oblivious transfer protocol to address the aforementioned problems. The proposed adaptive POT is built on top of a new cryptographic primitive, namely, adaptive set membership encryption (ASME). In our proposed protocol, all items are encrypted without the use of buyers' public keys and hence they can be used for universal buyers. Our protocol significantly reduces the transaction cost compared to existing schemes. For example, the communication in each transaction costs only 6 group elements compared to at least 141 group elements in Rial et al.'s protocol. The implementation shows that our protocol is efficient in terms of bandwidth and computational cost.

Filtering refers to an operation to determine whether the concerned data should be accepted and transferred, or be blocked and marked as a malicious traffic flow. It mitigates the inter-domain bandwidth overhead, local computational cost and storage cost for data identification. In many sensitive applications, the identity of the data provider needs to be hidden. This creates challenges how to filter the transmitted data packet with an encrypted form. It is non-trivial to hide this data provider's identity while enabling filtering, as the policy used as a matching criteria will need to determine whether the data needs to be transferred or not without knowing the origin of that data. In this work, we designPPFilter, a privacy-aware encrypted filtering mechanism which allows the filtering to be conducted without the need to know the identity of the data provider. PPFilter achieves the integrity protection of the data packets and the provider privacy Level 3. PPFilter is built on top of a novel notion calledidentity-based encryption with sender search (IESS), which supports anonymous sender identity in an encrypted searching. We present a provably secure IESS instantiation, and apply it to achieve a PPFilter protocol. PPFilter allows the data provider's identity to be hidden from both the transferred data and policy while enabling the filtering capability, which solves the aforementioned problem. The analysis and evaluation show that PPFilter maintains cost-reasonable filtering while preserving provider privacy, and hence it guarantees its practicality.

Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary pre-steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under various encryption mechanisms when data owners try to secure the confidentiality of the data. This makes data aggregation extremely challenging, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching service that securely bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data, i.e., after encryption switching one may search over the (outsourced) data without loss of data and query secrecy. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme may be applicable in multi-domain cloud storage system.

In the identity-based broadcast encryption (IBBE), only these users whose identities are chosen in the ciphertext computing can decrypt the encrypted message. In this paper, we introduce an extension of IBBE, namely Identity-Based Broadcast Encryption for Inner Product (IBBE-IP), where message encryption is replaced by inner product encryption (IPE) introduced by Abdalla et al. (PKC 2015). Precisely, in the IBBE-IP, the private key is associated with a pair of an identity and a vector (ID,y→)⁠. The user with private key of (ID,y→) can decrypt the encrypted vector x→ for an identity set S selected by the encryptor and learn the inner product 〈x→,y→〉 if and only if ID∈S⁠. Differing from the IBBE, the decryption in the IBBE-IP yields the inner product associated with the encrypted vector without leaking any information of the vector. The encrypted vector is protected as long as the number of selected identities is less than their length. We present a construction of IBBE-IP with constant-size private keys and it supports unbounded private key queries, which was unachieved in the previous works of IPE in the public-key setting. The security of our proposed scheme is proved in the random oracle model.

It is widely acknowledged that the collaborations with more users increase productivity. Secure cloud storage is a promising tool to enhance such a collaboration. Access control system can be enabled with attribute-based encryption. In this system, a user encrypts and uploads his/her data to the cloud with an access policy, such that only people who satisfy that access policy can decrypt the data. When a recipient would like to enable another person who is originally unauthorized by the original access policy, this recipient will need to extend the access policy by adding a new policy that includes the new person hence, the notion of extendable access control system. Admitting new users to access the uploaded data is an important requirement in enhancing collaborations. The main issue is with regards to the integrity protection during the process of extending the access policy. When a new access policy is added, the cloud has to be sure that the extended access policy remains guarding the same encrypted data as the original access policy, even though the cloud cannot decrypt this ciphertext, which is a challenging problem to solve. In this paper, we answer the above problem affirmatively by introducing an extendable access control system with Integrity Protection (EACSIP), which is suitable to enhance collaboration in the cloud. The construction of EACSIP is built on top of a novel cryptographic primitive, namely functional key encapsulation with equality testing. The security proof and the performance evaluation of EACSIP are provided in this paper.

The ciphertext retrieval is of paramount importance for data confidentiality and utilization in mobile cloud environment. The receiver, usually equipped with resource constrained devices, retrieves data stored in the cloud server by submitting a confidential request (or trapdoor) to the cloud. Previous schemes need at least one exponentiation operation in group |$\mathbb {G}$| for each keyword to generate the trapdoor, which is quite burdensome for mobile devices to support such computational cost. The computational cost of trapdoor generation limits the application of ciphertext retrieval, especially in a wireless environment. In this paper, we propose the first online/offline ciphertext retrieval (OOCR) scheme, where the trapdoor generation is split into two phases: offline phase and online phase . Most of the computation of the trapdoor could be performed in the offline phase prior to knowing the keyword. The generation of the real trapdoor with keyword can be done efficiently in the online phase. The most challenging task is to resist the so-called insider attacks, which is about keyword guessing attacks from the untrusted cloud server. We also build a novel framework to resist insider attacks and propose an OOCR scheme against insider attacks. Our semantic security proof and performance analysis demonstrate that the proposal is practical for mobile cloud applications.

Abstract Centralized approaches are widely adopted to improve the qualities of outsourced services owing to its feature of efficient system management. Because the cloud is untrusted, data are usually encrypted before outsourcing. One of the interesting applications is searchable encrypted keywords, a well‐known cryptographic primitive that allows encryption while enabling search for keywords. However, achieving data retrieval without revealing privacy in a cloud‐based centralized system is still a challenging problem. In this paper, we introduce centralized approaches to searchable encryption and present a novel centralized system for retrieval services. In our system, the centralized manager can search and access all the encrypted data from authorized users, while each user can only search and access his or her own data. The system builds on a new cryptographic notion calledcentralized keyword search on encrypted data. We formalize its security model and propose a centralized keyword search on encrypted data construction that is featured by short ciphertext and search result verification. We further extend the construction for removing secure channel and enabling batch authentication on data legalities. The experiment demonstrates the performance of our proposals. Copyright © 2016 John Wiley & Sons, Ltd.