Graphs effectively model interactions in real-world applications such as social and trade networks, where Graph Neural Networks (GNNs) excel at tasks such as link prediction to enhance user experiences. Despite these benefits, users raise privacy concerns as user data can be exploited to improve GNN performance without consent. Accordingly, various graph unlearning methods have been developed. Prior work shows that comparing models before and after unlearning enables attackers to launch former membership inference attacks (FMIA) on unlearned data. However, the imprint of unlearned data left in the unlearned model itself remains underexplored, and existing membership inference methods mainly exploit overfitting, making them ineffective for identifying unlearned data. To address this, we conducted theoretical analysis and proposed an attack framework targeting unlearned GNNs by learning the distribution patterns of unlearned data to distinguish them from normal test data. Extensive experiments on four real-world datasets and GNN architectures confirm our framework's effectiveness and reveal significant vulnerabilities in current graph unlearning methods.

Human Pose Estimation (HPE) is a core component in real-time decision systems, supporting critical applications such as healthcare monitoring, autonomous driving, and sports analytics. While deep learning models—particularly CNNs and Transformer-based architectures—have significantly improved HPE accuracy, they remain vulnerable to adversarial perturbations that subtly distort keypoint localization, thereby undermining system reliability. To address this challenge, we propose regularization and pruning transferable adversarial attack (RPA), a novel framework designed to enhance the transferability of adversarial samples in Transformer-based HPE models. RPA integrates two synergistic strategies: gradient regularization, which suppresses dominant feature correlations to reduce overfitting, and adaptive weight pruning, which removes redundant parameters to reduce model-specific noise. This dual mechanism enables the generation of transferable adversarial attacks that are effective across diverse model architectures. Extensive experiments on state-of-the-art HPE networks demonstrate that RPA consistently outperforms existing attack methods. In white-box settings, RPA reduces average precision (AP) by 0.05-0.30; in black-box scenarios, it yields AP drops of 0.01-0.04. These findings expose critical vulnerabilities in IoT-enabled HPE applications and establish a new benchmark for evaluating adversarial robustness in real-time perception systems.

Personalized Federated Learning (pFL) tailors models to each client’s local data distribution in heterogeneous federated learning settings. Federated Meta-Learning (FML) is a branch of pFL that uses meta-learning to achieve fast adaptation, where clients start with a meta-model and personalize it by fine-tuning it with local data. Since a single global meta-model has limitations when the data distribution of clients varies significantly, meta-model personalization should be considered in FML. However, most benchmark pFL methods lack meta-model personalization, and usually lack meta-learning or relying on a single global meta-model. Besides, these methods can neither provide meta-model personalization nor guarantee generalization and convergence, due to the challenges in measuring the distance between the meta-model and the client model in FML. To address these issues, we combine FML with hypernetwork and propose a constrained hypernetwork-based FML framework called FMLH, which innovatively utilizes hypernetwork to capture the differences in fine-tuned models, thereby providing personalized meta-models for each client. We provide rigorous mathematical proofs illustrating how the hypernetwork affects the convergence and generalization bounds of FMLH. Experimental results demonstrate that FMLH significantly improves the generalization of the model in cross-client shifts, with the lowest decile accuracy improved by up to 18.71%. FMLH also outperforms representative pFL algorithms by up to 5.6% in terms of maximum accuracy improvement.

The increase in the amount of data stored in the cloud leads to the need for privacy-preserving multi-keyword search schemes in the cloud. However, most of the existing schemes usually adopt the TF-IDF vector space model, in which the vectors are high-dimensional and sparse. It results in substantial computation time and storage space. To address the issue, we propose an efficient dictionary partition-based multi-keyword ranked search scheme (DPMRS) over encrypted cloud data. First, a dictionary partition-based vector space model (DPVSM) is designed, which can compress vector dimensions and hence accelerate relevance score computation between documents and search keywords. Based on DPVSM, a dictionary partition-based keyword distribution inverted index (DPKD-index) is presented. By using the index, a baseline privacy-preserving ranked search scheme is proposed. To further improve the efficiency of search services, the search tree structure is adopted and a novel double tier search tree-based index (DSTree-index) is designed. By using the optimized index, an enhanced search scheme (DPMRS+) is proposed. The security analysis indicates that the proposed scheme can protect the privacy of search processing, and the experimental results show that the proposed scheme outperforms the existing works in terms of storage size, search precision, and search time cost.

The burgeoning 5G network brings edge servers closer to users to host online applications. These edge servers are typically kept running 24/7 to meet users' computational demands. However, the user coverage, privacy assurance, and service delay have consistently undermined users' confidence, compounded by the significant environmental damage caused by excessive energy consumption. Recently, various approaches have been proposed to tackle the energy-saving demand response issue in the multi-access edge computing (MEC) system. Unfortunately, existing attempts often compromise service quality and energy efficiency for privacy enhancement, and incur significant computational overheads and delays unsuitable for real-time services. Therefore, maintaining satisfying user coverage with energy consumption while adhering to users' privacy demands with low computational overhead is critical to achieving sustainable edge services. To address those challenges, we systematically formulate the location-privacy-preserving edge demand response (LEDR) problem and introduce a novel system named KGEES. KGEES incorporates$k$-anonymity geo-obfuscation to enhance user privacy while leveraging a heuristic approach to finalize resource allocation strategies under geo-distortion greedily to jointly improve system utility, energy, and time efficiency. Comprehensive experiments on a real-world dataset demonstrate that KGEES surpasses the representative approaches by an average of$1.187 \times$in system utility and$1.192 \times$in energy efficiency while being$ 203.5 \times$faster.

Connected autonomous vehicles (CAVs) utilize multi-modal sensors, such as LiDAR and high-definition cameras, to collect diverse types of sensing data. Fusing object detection information from these two modalities facilitates more accurate environmental perception. In this context, lightweight secret sharing techniques are employed to protect information privacy, enabling further calculation while effectively alleviating the computational resource constraints of CAVs. Meanwhile, such techniques require an additional third-party to generate some necessary random numbers. Addressing the challenges of privacy disclosure of multi-modal object information and the reliability of random numbers, we propose a malicious third-party-resistant privacy-preserving multi-modal object fusion model, termed MPOF. First, we develop a series of secure computation protocols that do not rely on time-consuming cryptographic primitives, including secure multiplication, secure sharing conversion, and secure comparison. Leveraging the idea of sacrificial verification, we can effectively detect malicious behavior by the third-party during the random number generation process. Second, we construct a secure object bounding-box matching module based on arithmetic secret sharing (ASS), enabling similarity calculation and matching of bounding-boxes between point cloud and image modalities. Additionally, we design a secure object score fusion module that achieves fusion and updating through secure implementations of convolution, ReLU, and Maxout operations. Detailed theoretical analysis and experimental results demonstrate that, compared to secure computation protocols using homomorphic encryption for random number generation, the proposed protocols reduce computational overhead by five orders of magnitude. Furthermore, the MPOF model constructed by integrating these protocols is secure, accurate, and efficient.

Sharing relational databases is essential in today’s data-driven world for fostering collaboration, enhancing efficiency, and enabling real-time data access. However, privacy and copyright issues arise when sharing privacy-sensitive or valuable data. Additionally, high utility is required in shared data to enable accurate data mining and analysis. Entry-level differentially private fingerprinting schemes (DPFS) could address these concerns. In a DPFS, data can be securely shared without leaking original values while still supporting accurate analysis. Moreover, detectable fingerprints can deter unauthorized redistribution. However, existing DPFSs often lack utility—due to format changes and entry-wise bias—or robustness, as fingerprints can be removed undetected. In this paper, we propose an unbiased and robust differential privacy-based fingerprinting scheme (DPFS), which ensures that the fingerprinted copy remains an unbiased estimate of the original data. By incorporating differential privacy noise, our scheme effectively mitigates alteration, collusion, and hybrid attacks. Our DPFS satisfies ϵ-entry-level differential privacy, enabling clients to conduct unbiased analysis. To improve robustness, we design group-based fingerprint detection, which estimates the mean of injected noise per group with error tolerance. We provide a theoretical robustness analysis and propose a method for achieving optimal robustness. Experiments on four real-world databases show that our scheme consistently detects fingerprints and improves accuracy by up to 20% on machine learning tasks compared to existing DPFSs.

With the rise of the smart industry, machine learning (ML) has become a popular method to improve the security of the Industrial Internet of Things (IIoT) by training anomaly detection models. Federated learning (FL) is a distributed ML scheme that facilitates anomaly detection on IIoT by preserving data privacy and breaking data silos. However, poisoning attacks pose significant threats to FL, where adversaries upload poisoned local models to the aggregation server, thereby degrading model accuracy. The prevalence of non-independent and identically distributed (non-IID) data across IIoT devices further exacerbates this threat, as it naturally leads to diverse local models, making malicious ones harder to distinguish. To address the above challenges, we propose a deep-layer sign-sharing personalized FL (DSPFL) scheme. DSPFL innovatively aggregates only the signs of stochastic gradients (SignSGD) from the deep layers of local models during training. This targeted aggregation enhances the robustness of the shared components against poisoning attacks, while shallow layers are retained locally to preserve personalization. This integrated approach improves the accuracy and resilience of personalized local models on IIoT devices under poisoning attacks. Extensive experimental results show that DSPFL consistently achieves up to 20% higher and more stable overall personalized model accuracy compared to state-of-the-art methods under specific poisoning attacks.

Searchable encryption enables privacy-preserving queries over data outsourced to cloud services. Classical symmetric schemes deliver efficient search but largely assume a single client setting; multi-client variants permit delegation yet typically treat authorization as an owner-local decision, overlooking regulations enforced by higher-level authorities (e.g., sector-specific compliance). In practice, limited familiarity with regulatory detail or operational lapses can lead owners to delegate search permissions that violate authority regulations, rendering existing systems unsuited to regulated, multi-client cloud environments. To address this gap, we propose two systems. First, we propose SENTRY, a multi-client dynamic searchable encryption framework with a built-in compliance-check service via sanitized authorization. Its core is a tag-based sanitization protocol: the authority encodes prohibited keywords as hidden tags, and the sanitizer uses these tags to remove non-compliant per-keyword search permissions before they reach readers, without learning the underlying keywords. As a result, readers receive only compliant search capabilities. We then proposeF-SENTRY, a forward private variant of SENTRY for settings where regulations evolve over time.F-SENTRY preserves the same sanitized-authorization mechanism and further adds forward privacy through a tailored constrained shiftable encryption, which binds search permissions and newly added encrypted updates to regulatory epochs. Consequently, permissions issued before a policy change cannot be used to retrieve data added afterward unless they are refreshed for the new epoch. We formalize the security of both SENTRY andF-SENTRY and prove them secure under standard assumptions. Experiments under cloud-like workloads show that SENTRY achieves regulation-compliant authorization with modest over head, whileF-SENTRY provides stronger protection under changing regulations at practical additional cost.

Recent key-value (KV) caches are offloaded to programmable switches to offer high query processing performance. However, they suffer from both low accuracy in hot key detection and high latency in cache updates due to the strict limitations on switch registers. We propose TurboCache, a switch-accelerated KV cache with accurate hot key detection and fast cache updates. Our key idea is to leverage the switch recirculation capability to build a novel data structure that caches hot KV pairs. With this hardware-compatible cache data structure, TurboCache designs efficient data plane algorithms that accurately detects new hot keys and quickly updates its cache entirely within switch ASIC pipelines. We have implemented TurboCache on a${64}\times {100}$Gbps Tofino switch. Testbed results indicate that TurboCache improves the hot key detection accuracy and decreases the cache update latency of existing KV caches by several orders of magnitude.

Spiking Neural Networks (SNNs) have attracted significant attention from the research community due to their high energy efficiency compared to Artificial Neural Networks (ANNs). However, rare studies on the security of SNNs were conducted, especially in backdoor attacks. Existing defense methods for ANN backdoor attacks either perform poorly or can be easily bypassed in SNN scenarios due to SNNs’ event-driven and temporal dependency characteristics, posing significant research challenges. In this paper, we identify the blockers to existing backdoor defenses for defending against attacks in SNNs and propose an unsupervised post-training backdoor detection method named Temporal Membrane Potential Backdoor Detection (TMPBD) to address those blockers in SNNs with neuromorphic data. Specifically, TMPBD employs the maximum margin statistic of temporal membrane potential in the last spiking layer of the SNNs to detect attack target labels without knowledge of the attack or access to any data. Moreover, we also design a practical and robust mitigation mechanism named Neural Dendrites Suppression Backdoor Mitigation (NDSBM). NDSBM dually clamps the neural dendrites, i.e., the weights connecting the first two convolution layers in each convolution block to limit the backdoor effect, while preserving the benign model behaviors learned from the temporal membrane potential obtained from a small, clean, unlabeled dataset in the same domain. To evaluate the performance, we conduct a comprehensive evaluation with multiple backdoor attack techniques, including the SOTA input-aware dynamic trigger attack dedicated to SNNs with clean models on three neuromorphic benchmark datasets. The results demonstrated that TMPBD achieves 100% prediction accuracy in detecting dynamic trigger attacks and associating attack target labels in all benchmark datasets. NDSBM lowered the attack success rate (ASR) from 100% caused by the dynamic trigger attack down to 8.44% with only mitigation or 2.81% when combined with detection for an end-to-end pipeline without performance degradation in clean accuracy.

A Batch Private Information Retrieval (batch-PIR) scheme allows a client to retrieve multiple data items from a database without revealing them to the storage server(s). Most existing approaches for batch - Pirare based on batch codes, in particular, probabilistic batch codes (PBC) (Angel et al. S&P'18), which incur large storage overheads. In this work, we show that zero storage overhead is achievable for tree-shaped databases. In particular, we develop TreePIR, a novel approach tailored made for private retrieval of the set of nodes along an arbitrary root-to-leaf path in a Merkle tree with no storage redundancy. This type of tree has been widely implemented in many real-world systems such as Amazon DynamoDB, Google's Certificate Transparency, and blockchains. Tree nodes along a root-to-leaf path forms the well-known Merkle proof. TreePIR, which employs a novel tree coloring, outperforms PBC, a fundamental component in state-of-the-art batch-PIR schemes (Angel et al. S&P'18, Mughees-Ren S&P'23, Liu et al. S&P'24), in all metrics, achieving 3 ×lower total storage and 1.5-3 ×lower computation and communication costs. Most notably, TreePIR has 8-160× lower setup time and its polylog-complexity indexing algorithm is 19–160 ×faster than PBC for trees of 210_224leaves.

Dynamic graph neural networks (DGNNs) have emerged and been widely deployed in various web applications (e.g., Reddit) to serve users (e.g., personalized content delivery) due to their remarkable ability to learn from complex and dynamic user interaction data. Despite benefiting from high-quality services, users have raised privacy concerns, such as misuse of personal data (e.g., dynamic user-user/item interaction) for model training, requiring DGNNs to "forget" their data to meet AI governance laws (e.g., the "right to be forgotten" in GDPR). However, current static graph unlearning studies cannot unlearn dynamic graph elements and exhibit limitations such as the model-specific design or reliance on pre-processing, which disenable their practicability in dynamic graph unlearning. To this end, we study the dynamic graph unlearning for the first time and propose an effective, efficient, general, and post-processing method to implement DGNN unlearning. Specifically, we first formulate dynamic graph unlearning in the context of continuous-time dynamic graphs, and then propose a method called Gradient Transformation that directly maps the unlearning request to the desired parameter update. Comprehensive evaluations on six real-world datasets and state-of-the-art DGNN backbones demonstrate its effectiveness (e.g., limited drop or obvious improvement in utility) and efficiency (e.g., 7.23× speed-up) advantages. Additionally, our method has the potential to handle future unlearning requests with significant performance gains (e.g., 32.59× speed-up).

Federated Learning is an emerging distributed algorithm that is designed to collaboratively train the global model without accessing clients’ private data. However, heterogeneity of data among clients leads to significant degradation in model performance. Some studies suggest adopting model regularization and using generators to enrich datasets with diverse features can effectively enhance model performance. But current research focuses on regularizing specific modules of the model, failing to achieve regularization across the entire model, and offering limited mitigation of bias from heterogeneous data. Moreover, few methods consider that generators often produce samples with simple features, and the direct use for generating raw data can raise privacy concerns. To solve these challenges, we propose a generator-based heterogeneous Federated Prototype Adversarial Learning framework, named FedPA, which combines prototype learning and lightweight generators to achieve regularization of the entire model. Our generators are designed to generate features rather than raw data, and use prototype learning to find the hard features in an adversarial learning manner, thereby improving model performance. Experimental results show that FedPA improves test accuracy by 3.7% compared to state-of-the-art methods, validating that FedPA can effectively mitigate model bias.

Organizations these days capitalize on crowdsourcing to learn collective wisdom from a population of individuals. Vast amounts of data have been gathered, making the crowdsourcing platforms a lucrative target to steal data from and thus raising severe privacy concerns. Data contributed by workers may carry sensitive individual information. Meanwhile, organizations deem the aggregate statistics as intellectual property. In this paper, we propose, design, and evaluate GoCrowd, a system framework for obliviously aggregating wisdom with quality assurance in crowdsourcing. At its core, we propose constructions for two procedures. The starting point is a gold-standard based private worker quality control procedure that provides privacy-friendly worker quality assurance under the widely popular gold-standard mechanism. The subsequent procedure is an oblivious wisdom aggregation procedure that obliviously learns aggregate statistics over workers’ data while considering their quality. We securely realize these procedures with only lightweight secret sharing techniques. Our system is utterly oblivious to the service provider, and ensures that only the requester can learn the aggregate quality-aware statistics but nothing more. Extensive evaluations show that GoCrowd can produce quality statistics over data from 500 workers for 200 16-choice questions within 1 s.

Reputation-based authentication is a mechanism used to establish trust and ensure the reliability of vehicle communication in vehicular ad hoc networks (VANETs). However, existing schemes ignore the importance of privacy protection and face issues such as malicious vehicles spreading false or duplicate messages. To address these issues, this paper proposes a secure and anonymous reputation-based authentication scheme based on the ElGamal cryptosystem for the vehicular communication system, which promotes cooperative behavior and encourages vehicles' positive contribution to the network. In particular, we design an efficient duplicate detection mechanism based on the technique of Bloom filter to aid in identifying and isolating malicious vehicles. Security analysis is conducted to demonstrate the robust security of the proposed scheme. The results of performance evaluation highlight the superiority of the proposed scheme in addressing the identified shortcomings while maintaining reasonable computation and communication costs.

Decentralized identity (DID) is rapidly emerging as a promising alternative to centralized identity infrastructure, offering numerous real-world applications. However, existing DID systems are confronted with trust concerns, as any distributed node can act as a credential issuer and be considered trusted, which is impractical. Effective trust management (TM) protocols are critical for system trustworthiness but face two primary challenges: preserving user feedback privacy to meet regulation requirements and building resilience against trust attacks to prevent manipulation. While privacy-preserving TM protocols effectively safeguard sensitive data, they often obscure feedback, hindering anomaly detection and complicating efforts to counter trust attacks. To address these issues, we propose DidTrust, a novel decentralized identity trust management protocol that bridges data privacy and resilience to trust attacks. DidTrust features a feedback data privacy preservation protocol that conceals feedback data while maintaining authorizability and verifiability. It also implements countermeasures against cooperative and individual trust attacks, improving detection accuracy without compromising privacy. To improve efficiency, we introduce a feedback compression module for large-scale sparse matrices. Rigorous analysis proves DidTrust to be universally composable (UC) secure under a malicious model, and experiments demonstrate its improved computational and storage efficiency while achieving higher trust attack detection rates compared to BC-Trust.

While real-time electricity consumption data of users in smart grids can enable value-added services, such as Big Data analytics, each individual user's privacy needs to be protected. How to balance data utility and privacy protection is a significant issue, of which privacy-preserving data aggregation (PPDA) is a viable solution. Prior to this, researchers have proposed a number of PPDA schemes to address the above challenge. Unluckily, most of them suffer from security and privacy drawbacks, while others are inappropriate for resource-limited smart meters due to high cost of cryptographic operations. To tackle this issue, in this article, we propose a pairing-free and exponentiation-free certificateless PPDA scheme named CL-PPDA for smart grids. We prove the security of our design and analyze its performance. Comparative analyses with state-of-the-art work in theory and experiment show that our design not only has better security properties, but also has competitive computation overhead, especially on the resource-constrained smart meter side. Besides, we present an extension of our CL-PPDA scheme to support multidimensional data aggregation, which further enriches the functionality of our design.

Auditability and verifiability are critical elements in establishing trustworthiness in federated learning (FL). These principles promote transparency, accountability, and independent validation of FL processes. Incorporating auditability and verifiability is imperative for building trust and ensuring the robustness of FL methodologies. Typical FL architectures rely on a trustworthy central authority to manage the FL process. However, reliance on a central authority could become a single point of failure, making it an attractive target for cyber-attacks and insider frauds. Moreover, the central entity lacks auditability and verifiability, which undermines the privacy and security that FL aims to ensure. This article proposes an auditable and verifiable decentralized FL (DFL) framework. We first develop a smart-contract-based monitoring system for DFL participants. This monitoring system is then deployed to each DFL participant and executed when the local model training is initiated. The monitoring system records necessary information during the local training process for auditing purposes. Afterward, each DFL participant sends the local model and monitoring system to the respective blockchain node. The blockchain nodes representing each DFL participant exchange the local models and use the monitoring system to validate each local model. To ensure an auditable and verifiable decentralized aggregation procedure, we record the aggregation steps taken by each blockchain node in the aggregation contract. Following the aggregation phase, each blockchain node applies a multisignature scheme to the aggregated model, producing a globally verifiable model. Based on the signed global model and the aggregation contract, each blockchain node implements a consensus protocol to store the validated global model in tamper-proof storage. To evaluate the performance of our proposed model, we conducted a series of experiments with different machine learning architectures and datasets, including CIFAR-10, F-MNIST, and MedMNIST. The experimental results indicate a slight increase in time consumption compared with the state-of-the-art, serving as a tradeoff to ensure auditability and verifiability. The proposed blockchain-enabled DFL also saves up to 95% communication costs for the participant side.

Social video anomaly is an observation in video streams that does not conform to a common pattern of dataset's behaviour. Social video anomaly detection plays a critical role in applications from e-commerce to e-Iearning. Traditionally, anomaly detection techniques are applied to find anomalies in video broadcasting. However, they neglect the live social video streams which contain interactive talk, speech, or lecture with audience. In this paper, we propose a generic framework for effectively online detecting Anomalies Over social Video LI ve Streaming (AOVLIS). Specifically, we propose a novel deep neural network model called Coupling Long Short-Term Memory (CLSTM) that adaptively captures the history behaviours of the presenters and audience, and their mutual interactions to predict their behaviour at next time point over streams. Then we well integrate the CLSTM with a decoder layer, and propose a new reconstruction error-based scoring function REI A to calculate the anomaly score of each video segment for anomaly detection. After that, we propose a novel model update scheme that incrementally maintains CLSTM and decoder. Moreover, we design a novel upper bound and ADaptive Optimisation Strategy (ADOS) for improving the efficiency of our solution. Extensive experiments are conducted to prove the superiority of AOVLIS.

The global deployment of the 5G network has led to a substantial increase in the deployment of edge servers to host web applications, catering to the growing demand for low service latency by edge web users. Yet, running edge servers 24/7 leads to enormous energy consumption and excessive carbon emissions. Energy-efficient edge resource provision is desired to achieve sustainable development goals in the new multi-access edge computing (MEC) architecture. Recently, several approaches have been proposed to solve the demand response problem for energy saving in cloud computing and MEC. However, accurate location information of edge web users should always be provided, which sacrifices users' privacy. To protect edge web users' location privacy while saving energy in MEC, we systematically formulate this location privacy-preserving edge demand response (LEDR) problem. To solve the LEDR problem effectively and efficiently, we propose a system named GEES by incorporating differential geo-obfuscation to secure user privacy while maximizing system utility and energy efficiency through inferences with theoretical analysis. Extensive and comprehensive experiments are conducted based on a synthetic real-world dataset, and the results demonstrate that GEES outperforms representative approaches by 23.02%, 31.47%, and 17.29% on average in terms of energy efficiency, user privacy and system utility.

The rapid progress in the field of IoT and its wide-ranging applications emphasize the criticality of robust security measures for effectively sharing, storing, and managing sensitive data generated by IoT devices. Regulations such as the Consumer Data Rights (CDR) highlight the need for the seamless sharing of sensitive data with authorized third parties while ensuring confidentiality and privacy. To enable such secure sharing, a data storage and sharing scheme should fulfill the following core requirements: (a) support multi-client data sharing settings, allowing IoT data owners to authorize multiple clients; (b) a dynamic storage environment permitting IoT owners to add or remove files with minimal privacy leak; (c) decentralized storage for distributing data across servers or Cloud Service Providers (CSPs) for greater security; and (d) efficient privilege revocation mechanism which incurs less computation and communication overhead. To address these requirements, we have proposed a novel keyword search scheme using computationally lightweight cryptographic primitives. Our scheme empowers IoT data owners to securely share, store and manage encrypted data in the CSPs, providing better security and privacy. We have provided formal security proof for our scheme as well as validated its efficiency via extensive experiments on the Docker platform. On a database of 12 million keyword/document pairs (with 105 documents and 103 keywords), our scheme took about 18 ms to return all matched documents.

With significant achievements of Internet of Things (IoT) in cloud services, IoT devices are becoming primary targets of cyber attackers. To protect security of previous messages generated by IoT devices in cloud computing, puncturable attribute-based encryption (PABE) was introduced which achieves fine-grained access control and supports self-update to private keys to disable the decryption capability to existing ciphertexts. Users in PABE are identified by their attributes and authenticated via the attribute authority (AA) which issues them attribute keys. But PABE does not consider any attribute revocation which should be managed by the AA. To address such a concern, this article presents a primitive called PABE with user revocation (PAER) which is the first light-weight PABE scheme with the user revocation achieved by the AA broadcasting the information for the key update. In addition to the light calculation of users in the decrypting process, another prominent advantage in PAER is that the AA does not need to communicate with users to revoke their attributes but broadcasts regular updates in the network. We present a concrete construction of PAER, and then implement the given scheme to evaluate its practicability in the real world.

Over the past few years, the improved detection and processing features of Internet-of-Things (IoT) devices have opened the doors to several mobile crowdsourcing applications. Federated Learning (FL) is being seen as an attractive framework to address the data privacy concerns of mobile users in the context of crowdsourcing. In FL on a crowdsourcing platform, constructing an effective deep neural network (DNN) is challenging. This is primarily because the quality of the global model depends on the local model quality, which can vary greatly due to differences in the computational resources, data quantity, and data quality provided by each worker. To address these challenges, we propose QARMA-FL: Quality-aware robust model aggregation for federated learning in crowdsourcing applications, where we select the local model for aggregation based on its quality and performance. We also propose a model-quality-aware incentive mechanism to reward workers, based on their contribution to model training. Our model selection and incentive mechanism is capable of detecting Free Rider attacks, identifying workers who benefit from others contributions without contributing themselves. Most existing evaluations of FL in mobile crowdsourcing studies are not based on the real-world FL scenarios. Therefore, we evaluate QARMA-FL alongside a baseline FL model in a quantity-skew, non-IID data setup where different workers contribute varying amounts of data for model training. Our diverse experiments validated QARMA-FLs performance, demonstrating its ability to efficiently aggregate models in mobile crowdsourcing scenarios, reaching baseline results with a reduced worker participation by 40% to 60%.

This paper investigates data communication and access control in satellite-assisted Internet of Things. In particular, given the characteristics of an open communication environment, a multi-layer heterogeneous network, and a time-varying topology in the Space-Air-Ground-Sea Integrated Network (SAGSIN), traditional data communication and security mechanisms built upon the TCP/IP architecture may not fully leverage their potential. Current networks are primarily responsible for end-to-end transmission of binary data, lacking the capability to semantically perceive and handle dynamic and decentralized content. This leads to significant performance gaps in the network. In other words, it is better to retrieve expected information directly from the network and perform content protection on it with low dependency. We propose DARS, a Data Access scheme with Robust and Secure content communication for Satellite-assisted Internet of Things (S-IoT), leveraging the architectural benefits of content centric networks and rich attributes of IoT. DARS enables automatic data retrieval and access control without additional mechanisms, such as online certificate distribution, homogeneous network, and other presuppositions, which facilitates DARS to be applied in various environments. Additionally, DARS integrates a combination of techniques, including semantic representation, cryptographic technologies, and content caching, from a network-centric perspective. Theoretical analysis and experimental simulations show that DARS simplifies system operations and offers a viable solution for satellite-assisted IoT-based applications.

As an indispensable part of a smart city, the smart grid has gained widespread attention from industrial and academic communities. How to securely collect users’ real-time energy consumption data to provide services such as big data analytics and demand-response services while ensuring the privacy of individual users is a challenging issue in the smart grid. The privacy-preserving data aggregation (P2DA) suggests a feasible solution. For years, researchers have designed numerous P2DA schemes for securing smart grids. Unfortunately, the majority of them have some security and privacy deficiencies. Other schemes are unsuitable for resource-constrained smart meters due to expensive cryptographic operations. In this work, we design a lightweight verifiable certificate-based P2DA scheme LV-P2DA without pairings for smart grids. We formally prove its security under standard cryptographic assumptions. The performance comparison results illustrate that compared with state-of-the-art solutions, our design achieves at least a 99.43% improvement in computational cost and a 32.96% improvement in communication cost on the smart meter side, respectively.

For businesses looking to outsource their data to remote servers, cloud-based data storage is a popular choice. It is popular due to its flexibility, cost-effectiveness, and widespread availability. However, ensuring the confidentiality of data is a critical challenge that must be addressed. As a response to this issue, searchable encryption techniques have been developed. These techniques enable search queries to be performed on encrypted data while still keeping the plaintext confidential. While most existing symmetric searchable encryption schemes are designed for one-dimensional data records or document-keyword inverted indices, this paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. MDRSSE stands out as one of the pioneering SSE schemes to support multi-dimensional range search efficiently, without incurring undetermined additional communication or computation costs. By employing a single round of communication between the client and server, MDRSSE enables an honest-but-curious server to respond to multi-dimensional range queries without gaining knowledge of the data records or revealing the search query. Notably, MDRSSE boasts the lowest overall search complexity compared to existing state-of-the-art symmetric searchable encryption schemes designed for multi-dimensional range search. Extensive experimental tests were conducted to validate the robustness and practicality of our proposed scheme. The results demonstrate that, for a dataset consisting of 100K records with 12 dimensions (with each leaf node holding 500 records), it takes only 2.2 seconds to generate the encrypted dataset, and the overall setup phase completes within 2.5 seconds. Furthermore, for a range query encompassing 50 nodes, the search time is less than 2 ms and 3 ms for the client and server, respectively. MDRSSE achieves semantic security under the IND-CPA assumption, all without requiring additional storage size at the server.

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.

Since its inception, blockchain technology has found wide-ranging applications in various fields including agriculture, energy, and so on, owing to its immutable and decentralized nature. However, existing blockchains encounter significant challenges in scenarios that demand efficient retrieval of big data. This is primarily because current blockchains cannot directly store and process diverse types of rich media information. Additionally, the semantic relationships between data within the blockchains are weak, complicating the categorization and retrieval of data and transactions. Moreover, the scalability of current blockchains is limited, with the capacity of full nodes continually increasing. Although some semantic-based blockchain solutions that combine off-chain scalability have been proposed, they are limited in effectiveness and applications. To address these issues, this paper introduces a brand-new blockchain sharding technique called Semantic Sharding, which enhances blockchain scalability through a hybrid on/off-chain approach. Building on this, we propose a semantic sharding blockchain architecture, SemantiChain, which enables the on-chain storage and retrieval of transaction semantic features. Furthermore, through the Po2RW consensus protocol, we balance the scalability and security of SemantiChain. Security analysis proves that SemantiChain can resist security risks such as man-in-the-middle attacks, malicious node attacks and on/off-chain data inconsistency. Experimental results demonstrate that SemantiChain can reduce search time and memory usage by at least 32.29% and 77.97% respectively under the same retrieval performance, compared to mainstream approximate nearest neighbour retrieval algorithms. Furthermore, compared to the SOTA semantic blockchain, SemantiChain achieves a retrieval performance improvement of at least 45.88% and reduces retrieval memory usage by 95.76%.

We propose a novel approach to effectively detect cloned identities of social-sensor cloud service providers (i.e. social media users) in the face of incomplete non-privacy-sensitive profile data. Named ICD-IPD, the proposed approach first extracts account pairs with similar usernames or screen names from a given set of user accounts collected from a social media. It then learns a multi-view representation associated with a given account and extracts two categories of features for every single account. These two categories of features include profile and Weighted Generalised Canonical Correlation Analysis (WGCCA)-based features that may potentially contain missing values. To counter the impact of such missing values, a missing value imputer will next impute the missing values of the aforementioned profile and WGCCA-based features. After that, the proposed approach further extracts two categories of augmented features for each account pair identified previously, namely, 1) similarity and 2) differences-based features. Finally, these features are concatenated and fed into a Light Gradient Boosting Machine classifier to detect identity cloning. We evaluated and compared the proposed approach against the existing state-of-the-art identity cloning approaches and other machine or deep learning models atop a real-world dataset. The experimental results show that the proposed approach outperforms the state-of-the-art approaches and models in terms of Precision, Recall and F1-score.

With the rise of 5G technology, Mobile (or Multi-Access) Edge Computing (MEC) has become crucial in modern network architecture. One key research area is the effective placement of edge nodes, which has attracted significant attention. Service providers strive to minimize deployment costs for these nodes within a network. Although many studies have explored optimal strategies for reducing these costs, most overlook the allocation of computational resources and the users’ tolerance for delays. These factors add complexity, making previous methods less adaptable. In this paper, we define the Cost Minimization in MEC Edge Node Placement problem. Our goal is to find the optimal strategy for deploying edge nodes that minimize costs while cater to users’ delay tolerance limits. We prove the NP-hardness of this problem and provide a range of solutions, including Cluster-based Mixed Integer Programming, Coverage First Search, and Distance-Aware Coverage First Search, to address this challenge effectively and efficiently. Additionally, we propose a fine-grained optimization approach for allocating computational resources to edge nodes based on user service requests, significantly lowering deployment costs. Extensive experiments on a large-scale real-world dataset show that our solutions outperform the state-of-the-art in efficiency, effectiveness, and scalability.

Reputation systems are widely used to provide a trustworthy environment and improve the sustainability of online discussions. They help users understand and evaluate the quality of information by collecting and counting feedback from different users. However, a common issue in most reputation systems is how to maintain users’ reputation and protect their anonymity simultaneously. In this paper, we introduce a new practical anonymous reputation system based on SGX. The establishment of an anonymous reputation system has a positive effect on sustainable trust in reputation-based online applications. Our system achieves the combination of reputation and anonymity by utilizing Intel SGX and the Bloom filter. The Path ORAM algorithm is also implemented to resist side-channel attacks. The experiments demonstrate that our system achieves high performance in terms of computation and storage costs. When compared to two state-of-the-art anonymous reputation systems, our system has better computation performance with at least three orders of magnitude.

Abstract Recommender systems have been widely used for implementing personalised content on many mobile online services to reduce computational overload and preserve wireless data for users. The underlying mechanisms used for building recommender systems analyse data collected from users to make recommendations. This poses concerns over the privacy of data from users as both service providers and the cloud will have access. Privacy-preserving recommender systems protect user information by incorporating various cryptographic mechanisms to prevent accessing the data. However, existing works are not practical due to the use of heavy cryptography. In this paper, we propose an efficient privacy-preserving recommender system that takes advantage of clustering to improve efficiency. Using a secure clustering mechanism, user data are assigned to multiple clusters before being fed into the recommendation. Our proposed protocols are privacy-preserving and do not leak information that could be used to identify a data subject. The experiments show that our system is efficient and accurate.

Abstract Privacy-enhancing techniques and protocols for data aggregation and analytics in wireless networks require the development of novel methods for efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active attackers. Previous approaches to secure privacy-preserving computation of distributed queries incur significant communication overhead and cannot be applied to big data. This paper proposes two solutions to the problem of efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active outsider attackers for a new large class of distributed statistical or numerical queries. This class contains many useful statistics and is larger than other classes considered in the literature previously. We propose two protocols for the Protection of data from Active Attackers (PAA) in the case of distributed privacy-preserving computation: PAA applying Shamir’s Secret Sharing (PAA-SSS) and PAA applying homomorphic encryption (PAA-HE). The PAA-HE protocol combines the use of ElGamal and Paillier encryption schemes in one system. Theoretical analysis and experimental results show that both protocols outperform alternative approaches. PAA-HE provides stronger protection and is more efficient than PAA-SSS.

Traditional searchable encryption schemes for clouds are generally based on TF-IDF vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It will lead to substantial computational cost of inner product, and thus slows the search speed. In this paper, we propose a two-layer fast search index-based multi-keyword ranked search scheme (TFSRS) to address this problem. In the proposed TFSRS scheme, a keyword clustering-based equal-length dictionary partition (KCEDP) strategy is adopted to compress the document and search vectors, which benefits the process of inner product. Based on the strategy, a novel KCEDP-based vector space model (KCEDP-VSM) is proposed, and based on which, a two-layer fast search index (TFS-index) is presented. By using the TFS-index, secure inner product and symmetric encryption, the efficient multi-keyword ranked search scheme over encrypted cloud data is proposed. Experimental results show the better performance of the proposed schemes in search efficiency.

Industry 4.0 is all about doing things in a concurrent, secure, and fine-grained manner. Internet of Things edge sensors and their associated data play a predominant role in today’s industry ecosystem. Breaching data or forging source devices after injecting advanced persistent threats (APTs) damages the industry owners’ money and loss of operators’ lives. The existing challenges include APT injection attacks targeting vulnerable edge devices, insecure data transportation, trust inconsistencies among stakeholders, incompliant data storing mechanisms, etc. Edge servers often suffer because of their lightweight computation capacity to stamp out unauthorized data or instructions, which, in essence, makes them exposed to attackers. When attackers target edge servers while transporting data using traditional public-key infrastructure-rendered trusts, consortium blockchain (CBC) offers proven techniques to transfer and maintain those sensitive data securely. With the recent improvement of edge machine learning, edge devices can filter malicious data at their end, which largely motivates us to institute a blockchain and artificial intelligence-aligned APT detection system. The unique contributions of this article include efficient APT detection at the edge and transparent recording of the detection history in an immutable blockchain ledger. In line with that, the certificateless data transfer mechanism boosts trust among collaborators and ensures an economical and sustainable mechanism after eliminating existing certificate authority. Finally, the edge-compliant storage technique facilitates efficient predictive maintenance. The respective experimental outcomes reveal that the proposed technique outperforms the other competing systems and models.

Recommender systems have become ubiquitous in many application domains such as e-commerce and entertainment to recommend items that are interesting to the users. Collaborative Filtering is one of the most widely known techniques for implementing a recommender system, it models user–item interactions using data such as ratings to predict user preferences, which could potentially violate user privacy and expose sensitive data. Although there exist solutions for protecting user data in recommender systems, such as utilising cryptography, they are less practical due to computational overhead. In this paper, we propose RSUC, a privacy-preserving Recommender System based on User Classification. RSUC incorporates homomorphic encryption for better data confidentiality. To mitigate performance issues, RSUC classifies similar users in groups and computes the recommendation in a group while retaining privacy and accuracy. Furthermore, an optimised approach is applied to RSUC to further reduce communication and computational costs using data packing. Security analysis indicates that RSUC is secure under the semi-honest adversary model. Experimental results show that RSUC achieves 4× performance improvement over the standard approach and offers 54× better overall performance over the existing solution.

Neural network (NN) inference services enrich many applications, like image classification, object recognition, facial verification, and more. These NN inference services are increasingly becoming an essential offering from cloud computing providers, where end-users’ data are offloaded to the cloud for inference under a customized model. However, current cloud-based inference services operate on clear inputs and NN models, raising paramount privacy concerns. Individual user data may contain private information that should always remain confidential. Meanwhile, the NN model is deemed proprietary to the model owner as model training requires substantial resources. In this article, we present, tailor, and evaluateSonic, a lightweight secure NN inference service delegated in the cloud.Sonicleverages the cloud computing paradigm to fully outsource the secure inference, freeing end devices and model owners from being actively online for assistance.Sonicguards both user input and model privacy along the whole service flow. We design a series of secure and efficient NN layer functions purely using lightweight cryptographic primitives. Extensive evaluations demonstrate thatSonicachieves up to$60\times$bandwidth saving in online inference compared to prior art.

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.

In this article, we propose a trustworthy privacy-preserving federated learning (FL)-based deep learning (DL) service framework for Industrial Internet of Things-enabled systems. FL mitigates the privacy issues of the traditional collaborative learning model by aggregating multiple locally trained models without sharing any datasets among the participants. Nevertheless, the FL-based DL (FDL) model cannot be trusted as it is susceptible to intermediate results and data structure leakage during the model aggregation process. The proposed framework introduces an edge and cloud-powered service-oriented architecture identifying the key components and a service model for residual networks-based FDL with differential privacy for generating trustworthy locally trained models. The service model decomposes the functionality of the overall FDL process as services to ensure trustworthy execution through privacy preservation. Finally, we develop a privacy-preserving local model aggregation mechanism for FDL. We perform several experiments to assess the performance of the proposed framework.

This article proposes a blockchain-based federated learning (FL) framework with Intel Software Guard Extension (SGX)-based trusted execution environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.

The advancement of internet and communication technologies has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of Internet of Things-enabled medical imaging devices for early disease detection has enabled medical practitioners to increase healthcare institutions' quality of service. However, Healthcare 4.0 is still lagging in artificial intelligence and big data compared to other Industry 4.0 due to data privacy concerns. In addition, institutions' diverse storage and computing capabilities restrict institutions from incorporating the same training model structure. This article presents a secure multiparty computation-based ensemble federated learning with blockchain that enables heterogeneous models to collaboratively learn from healthcare institutions' data without violating users' privacy. Blockchain properties also allow the party to enjoy data integrity without trust in a centralized server while also providing each healthcare institution with auditability and version control capability.

By adopting advanced Internet of Things (IoT) technology to sense and collect traffic-related information to improve traffic safety and efficiency, the vehicular ad hoc network (VANET) is becoming a prominent application that changes human driving experiences in the current era. Because frequent data exchange occurs in open environments, VANETs are inherently vulnerable to security and privacy attacks. In history, many certificateless aggregate signature (CLAS) schemes with conditional privacy-preserving (CPP) have been proposed to ensure the authenticity and integrity of the exchanged data and protect users’ privacy. However, we reveal that the state-of-the-art schemes cannot be deployed in practical VANET applications by proposing concrete signature forgery attacks. To this end, we propose a new CLAS-based authentication scheme with CPP for VANETs. The rigorous security proofs based on the standard cryptographic assumption show that the scheme has enhanced security. Moreover, theoretical analysis and experimental evaluation illustrate the practicality of our design.

Analytics over social graphs allows to extract valuable knowledge and insights for many fields like community detection, fraud detection, and interest mining. In practice, decentralized social graphs frequently arise, where the social graph is not available to a single entity and is decentralized among a large number of users, each holding only a limited local view about the whole graph. Collecting the local views for analytics of decentralized social graphs raises critical privacy concerns, as they encode private information about the social interactions among individuals. In this paper, we design, implement, and evaluate PrivGED, a new system aimed at privacy-preserving analytics over decentralized social graphs. PrivGED focuses on the support for eigendecomposition, one popular and fundamental graph analytics task producing eigenvalues/eigenvectors over the adjacency matrix of a social graph and benefits various practical applications. PrivGED is built from a delicate synergy of insights on graph analytics, lightweight cryptography, and differential privacy, allowing users to securely contribute their local views on a decentralized social graph for a cloud-based eigendecomposition analytics service while gaining strong privacy protection. Extensive experiments over real-world social graph datasets demonstrate that PrivGED achieves accuracy comparable to the plaintext domain, with practically affordable performance superior to prior art.

The healthcare Internet of Things (IoT) is rapidly becoming an invaluable tool in the healthcare industry. However, sharing data in healthcare IoT raises many security and privacy concerns, such as how to ensure data integrity, source authentication, and data privacy. Redactable signature schemes (${{\sf RSS}}$s) could be a feasible solution to address this question because it allows a signature holder to independently delete the privacy-sensitive part of the authenticated data without invalidating the respective signature. This flexible data sharing mechanism not only protects data privacy but also saves bandwidth. However, the state-of-the-art${{\sf RSS}}$s suffer from either the costly public key management problem or the secret key escrow problem. Another drawback of these schemes lies in their computation and communication overheads and hence are quite expensive for constrained devices. To address these challenging issues, in this work, we first propose the notion of certificateless${{\sf RSS}}$. We then provide an efficient instantiation of our scheme and prove its security under cryptographic assumptions. Our construction supports batch verification and redaction control, which further saves bandwidth and enhances the security of shared data by preventing the dishonest holder from arbitrarily editing data. Moreover, the comparison analysis of theory and experiment with more recent works shows the practicability of our design.

Abstract The edge authentication of graphs has been studied in the literature because graphs are one of the most widely used data organization structures. The majority of such schemes cannot be used to authenticate general directed graphs (GDGs); other schemes cannot be used for addressing either the issue of dynamic update or the issue of information leakage (such as the existence of nodes/edges and structural relationship of the graph). Also, all the existing schemes do not consider the forward security: if the signer’s secret key has been compromised, all previously generated signatures remain valid. This property provides high-level security protection for authentication schemes. To address these issues, in this work, we propose a forward-secure edge authentication scheme for GDGs. Observe that existing such schemes can only give a proof such that ‘there is an edge between nodes $u$ and $v$’. Our scheme, however, can directly give a proof such that ‘there is no edge between nodes $u$ and $v$’, which makes the function of edge authentication schemes more diverse. Moreover, our proposed scheme is proven to be secure against an adaptive chosen-message adversary in the random oracle model. To show its desirable performance, we analyze the computational costs of our scheme and compare it with other related schemes in terms of features.

Wireless body area networks (WBANs) technology nowadays has become a promising networking paradigm in the Internet of Things (IoT) as it can provide people with high quality of life and high level of medical service. In order to ensure the security and privacy of patients’ sensitive biomedical data and the efficiency of message processing across different devices, it is critical to provide a secure and lightweight authentication scheme for WBANs. In this article, we propose an extra lightweight authentication scheme for mobile-edge computing-enabled WBANs. Two different authentication phases based on the modular square roots technique are designed: one is the intra-BAN authentication between the sensor node and edge node (EN), and the other is the inter-BAN authentication between EN and application provider. The proposed scheme offers robust security by providing comprehensive security analysis. Performance is also evaluated in terms of computation, communication, and storage costs. The evaluation results demonstrate that the proposed scheme achieves a reduction of at least 90% in computation cost and at least 30% in communication cost when compared to four other related schemes.

Internet of Vehicles (IoV), a special form of Internet of Things (IoT), is an important enabler of intelligent transportation system, which is one of the most strategic applications in smart city initiatives. In order to achieve its intended functionalities, IoV requires anytime anywhere connectivity, which cannot be satisfied by traditional networking technologies. Space–air–ground-integrated network (SAGIN) is widely believed to be an ideal infrastructure for connecting IoV. In this article, we present an approach for understanding the security issues of complex IoT systems, and propose a security reference architecture for assessing security risks and addressing the security requirements. Specifically, we propose an activity-network-things (ANT)-centric security reference architecture, which is based on the three architectural perspectives in studying IoT systems, namely, device, Internet, and semantic. We discuss the limitations of existing IoT system architecture models, which are mainly evolved from the enterprise system architecture with some adaptation to the inherent features of IoT systems. Our approach can help manage the security risks by focusing on the critical activities performed in different microperimeters within an IoT system. The proposed architecture includes an organized process to understand the security requirements and select specific parameters for tailored security controls that are commensurate with organization-specific and application-specific security impacts of IoT. Our architecture is flexible enough to cater for any IoT application, and hence, can be easily applied to the case of SAGIN-enabled IoV.

Over the past decade, the Internet of Things (IoT) is widely adopted in various domains, including education, commerce, government, and healthcare. There are also many IoT-based applications drawn significant attentions in recent years. With the increasing numbers of the connected devices in the IoT system, one of the challenging tasks is to ensure devices’ authenticity, which allows users to have a high confidence in the decision. In addition, due to the heterogeneity of the IoT system and the resource-constrained devices, how to efficiently manage such system and guarantee the security and privacy for devices is concerned. In this article, we proposed a new blockchain-based authentication scheme to meet the challenges. Our proposed framework combines the blockchain technique and the modular square root algorithm to achieve an effective authentication process. Besides, we demonstrate the security and utility of the proposed scheme by providing the security analysis and the detailed experiment.

Wireless medical sensor networks (WMSNs) have aroused widespread attention in recent years with the development of Internet of Things (IoT) technology. WMSNs offer many new opportunities for healthcare professionals to monitor patients and patient self-monitoring. To overcome the resource (such as memory and power) limitations of sensors and attain data security of patients’ private medical information, researchers have designed plenty of work for securing WMSNs. For years, certificate-based aggregate signature (CBAS) schemes have been put forward for WMSNs to prevent patients’ sensitive medical data from being tampered with and damaged. In this work, we analyze the security flaws of a very recent CBAS scheme proposed by Vermaet al.(2021) by presenting two types of security attacks. We later propose a CBAS scheme with user anonymity protection for WMSNs and prove its security based on the standard cryptographic assumption. The performance comparison results from theory and experiment illustrate the practicality of our design.

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.

In this paper, we propose CryptMed, a system framework that enables medical service providers to offer secure, lightweight, and accurate medical diagnostic service to their customers via an execution of neural network inference in the ciphertext domain. CryptMed ensures the privacy of both parties with cryptographic guarantees. Our technical contributions include: 1) presenting a secret sharing based inference protocol that can well cope with the commonly-used linear and non-linear NN layers; 2) devising optimized secure comparison function that can efficiently support comparison-based activation functions in NN architectures; 3) constructing a suite of secure smooth functions built on precise approximation approaches for accurate medical diagnoses. We evaluate CryptMed on 6 neural network architectures across a wide range of non-linear activation functions over two benchmark and four real-world medical datasets. We comprehensively compare our system with prior art in terms of end-to-end service workload and prediction accuracy. Our empirical results demonstrate that CryptMed achieves up to respectively 413 ×, 19 ×, and 43 × bandwidth savings for MNIST, CIFAR-10, and medical applications compared with prior art. For the smooth activation based inference, the best choice of our proposed approximations preserve the precision of original functions, with less than 1.2% accuracy loss and could enhance the precision due to the newly introduced activation function family.

Medical time series data analytics based on dynamic time warping (DTW) greatly benefits modern medical research. Driven by the distributed nature of medical data, the collaboration of multiple healthcare institutions is usually necessary for a sound medical conclusion. Among others, a typical use case is disease screening for public health, where multiple healthcare institutions wish to collaboratively detect over their joint datasets the patients whose medical records have similar features to the given query samples. However, sharing the medical data faces critical privacy obstacles with the increasingly strict legal regulations on data privacy. In this article, we present the design of a novel system enabling privacy-preserving DTW-based analytics on distributed medical time series datasets. Our system is built from a delicate synergy of techniques from both cryptography and data mining domains, where the key idea is to leverage observations on the advancements in plaintext DTW analytics (e.g., clustering and pruning) to facilitate the scalable computation in the ciphertext domain, through our tailored security design. Extensive experiments over real medical time series datasets demonstrate the promising performance of our system, e.g., our system is able to process a secure DTW query computation over 15K time series sequences in 34 minutes.

The advances in machine learning have revealed its great potential for emerging mobile applications such as face recognition and voice assistant. Models trained via a Neural Network (NN) can offer accurate and efficient inference services for mobile users. Unfortunately, the current deployment of such service encounters privacy concerns. Directly offloading the model to the mobile device violates model privacy of the model owner, while feeding user input to the service compromises user privacy. To address this issue, we propose Leia, a lightweight cryptographic NN inference system at the edge. Leia is designed from two mobile-friendly perspectives. First, it leverages the paradigm of edge computing wherein the inference procedure keeps the model closer to the mobile user to foster low latency service. Specifically, Leia’s architecture consists of two non-colluding edge services to obliviously perform NN inference on the encoded user data and model. Second, Leia’s realization makes the judicious use of potentially constrained computational and communication resources in edge devices. We adapt the Binarized Neural Network (BNN), a trending flavor of NN with low inference overhead, and purely choose the lightweight secret sharing techniques to realize secure blocks of BNN. We implement Leia and deploy it on Raspberry Pi. Empirical evaluations on benchmark and medical datasets via various models demonstrate the practicality of Leia.

With the widespread adoption of cloud computing, it is increasingly popular for online social network (OSN) service providers to leverage the public cloud as a back-end to manage their services for the cloud’s well-understood benefits. However, the cloud is also notoriously subject to a wide attack surface, making it an imperative need to embed security in the cloud-backed OSN service from the very beginning. In light of this, in this paper, we design, implement, and evaluate PeGraph, the first system simultaneously allowing private, efficient, and rich queries over encrypted social graphs. PeGraph is aimed at safeguarding the confidentiality of the social graph at the cloud, while preserving the functionality of social search, a key enabler for quality OSN services like friend discovery and user targeting. PeGraph is built from a delicate synergy of insights from social graph modelling and lightweight cryptography such as searchable encryption and additive secret sharing, supporting rich social search queries like exact queries, fuzzy queries, and mixed queries. PeGraph also allows the cloud to obliviously render the encrypted social search results in a ranked order according to their importance, as per users’ preferences. Extensive experiments demonstrate that PeGraph can securely process a wide range of practical social search queries within 1 second, over a real-world social graph consisting of millions of entities.

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.

As a promising technology in the development of human healthcare services, the wireless body area networks (WBANs) technology has attracted widespread attention in recent years from both industry and academia. However, due to the sensitiveness of the medical system and the capability limitation of the wearable devices, security, privacy, and efficiency of the healthcare services in WBANs are remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. In this article, we firstly provide a review and cryptanalysis on the state-of-the-art authentication scheme. In order to meet the challenges and address the drawbacks in previous works, we then propose a new efficient and anonymous authentication scheme for cloud based WBANs. Through the security analysis, we show that our scheme could overcome the weaknesses in previous schemes and meet all the security requirements. Besides, we show the advantages of the proposed scheme through performance evaluation in terms of functionality features, computation overhead, communication overhead and storage overhead, which shows our scheme is more appropriate for practical applications on healthcare services.

Social sensing is a paradigm that allows crowd-sourcing data from humans and devices. This sensed data (e.g. social network posts) can be hosted in social-sensor clouds (i.e. social networks) and delivered as social-sensor cloud services (SocSen services). These services can be identified by their providers' social network accounts. Attackers intrude social-sensor clouds by cloning SocSen service providers' user profiles to deceive social-sensor cloud users. We propose a novel unsupervised SocSen service provider identity cloning detection approach, NPS-AntiClone, to prevent the detrimental outcomes caused by such identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks to perform cloned identity detection. It consists of three main components: 1) a multi-view account representation model, 2) an embedding learning model and 3) a prediction model. The multi-view account representation model forms three different views for a given identity, namely a post view, a network view and a profile attribute view. The embedding learning model learns a single embedding from the generated multi-view representation using Weighted Generalized Canonical Correlation Analysis. Finally, NPS-AntiClone calculates the cosine similarity between two accounts' embedding to predict whether these two accounts contain a cloned account and its victim. We evaluated our proposed approach using a real-world dataset. The results showed that NPS-AntiClone significantly outperforms the existing state-of-the-art identity cloning detection techniques and machine learning approaches.

Wireless body area networks (WBANs) technology nowadays has become a promising networking paradigm in the Internet of Things (IoT) as it can provide people with high quality of life and a high level of medical service. Specifically, to make sure those people with a high incidence of chronic diseases, including hypertension, diabetes, and cardiovascular diseases, are taking care of which further reduces social costs. Thus, both industry and academia in recent years pay widely attention to WBANs technology. After years of research, security, privacy, and efficiency of the healthcare services in WBANs have remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. To ensure the security and privacy of patients’ sensitive biomedical data and the efficiency of message processing across different devices, it is critical to provide a secure and lightweight authentication scheme for WBANs.

Establishing trust in the election outcome is a very important problem. In order to solve it, we propose a practical ranked choice online voting system where every user can verify the validity of the outcomes. The proposed system combines the use of public blockchain and Intel Software Guard Extensions (SGX) to ensure that all standard requirements for voting systems are met, and at the same time to provide protection against malicious adversaries with administrative access. Each cast vote is submitted to the Intel SGX via a secure communication channel. The SGX verifies the eligibility and validity of the vote and encrypts it inside the SGX enclave. Later on, only the fingerprints (i.e., the hash values) of the encrypted votes are published on the public blockchain ledger before the deadline of the election. All encrypted votes are released after the deadline. Every voter can self-tally all submissions in encrypted form using the homomorphic property of our encryption system. The proposed protocol also achieves end-to-end voter verification. Our theoretical security analysis and experimental results show that the protocol is secure and is more efficient than previous alternative voting protocols.

Abstract Biometric information is unique to a human, so it would be desirable to use the biometric characteristic as the private key in a cryptographic system to protect data security and privacy. In this paper, we introduce a notion called two-factor decryption (TFD). Informally speaking, a TFD scheme is a variant of the public-key encryption (PKE) scheme. In a TFD scheme, messages are encrypted under public keys as that in a standard PKE scheme, but both private keys (i.e. the first factor) and biometric inputs (i.e. the second factor) are required to decrypt the ciphertexts and obtain the underlying plaintexts. We first describe a framework of TFD, and then define a formal security model for TFD. Thereafter, we present a generic construction on TFD based on the cryptographic primitives of linear sketch and functional encryption (FE) with certain properties and analyse its security. In addition, we give instantiations of TFD by applying concrete FE schemes into the generic construction and show their applications.

In a typical e-healthcare system, it is common for users' physiological data collected by Internet-of-Things (IoT) devices to be processed and shared in a third-party environment. To improve service quality, healthcare data sharing in third-party environments needs to ensure the integrity, source authentication, and privacy of the data. Redactable signature schemes (RSSs) are designed to address this concern over the past decades. More concretely, an RSS allows a signature holder to delete privacy-sensitive parts of the signed data and derive a valid signature for the retained data without any help from the original signer. This also provides a flexible data sharing mechanism in a bandwidth-saving manner. However, almost all of the existing RSSs are built on top of public-key infrastructure (PKI) systems, which involve heavyweight public-key management problems and are not suitable for resource-limited IoT applications. Besides, we argue that the only known PKI independent RSS for IoT has some security flaws and requires a large storage space. In this work, we eliminate some of the costs associated with PKI and certificates (such as key managements and certificate verifications) in traditional RSS and propose the first identity-based RSS satisfying the requirements of protecting the integrity and source authentication with selective disclosure control for healthcare data sharing in IoT. We prove the security of the scheme in the random oracle model under the k-SDH assumption. Theoretical comparison and experimental analysis show that our construction has a practical performance. As an extension, we also discuss how to extend our design to achieve fine-grained redaction control, which provides a feasible strategy for a signer to prevent additional redaction or arbitrary redaction from dishonest signature holders.

We present a reversible biosignal steganography method to authenticate the source of biosignal in this paper. Cloud is being a popular platform for storing a large volume of biosignals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmogram (PPG). However, outsourcing biosignals to the cloud may introduce authenticity issues. For instance, patient data can be altered, or fake patient data can be inserted by the dishonest cloud service provider or attacker for giving benefits to business organizations such as insurance service providers. Steganography approaches can be used to hide data source's identification data before outsourcing to the cloud for maintaining authenticity. Existing biosignal steganography approaches fail to reconstruct original biosignal after applying a reverse data hiding technique. In other words, current biosignal steganography approaches are irreversible. Reversible biosignal steganography method is required for protecting biosignal data from deterioration and efficient use by its stakeholders. In this work, we develop a reversible biosignal steganography approach using the Extended Binary Golay Code based error correction method. Our proposed method embeds secret authentication message as an error within different types of biosignals such as ECG, PPG, and EEG. Extended Binary Golay Code based error correction method is used to extract the secret message, and reconstruct original biosignal. We conduct a set of experiments for evaluating the performance of our proposed method.

The personalized Web service recommendation based on Quality of Service (QoS) is gaining increasing popularity due to its promising ability to help users find high quality services. Studies suggest that it is beneficial to use Collaborative Filtering (CF)-based techniques to facilitate Web service recommendations which can achieve high accuracy in predicting the QoS for unobserved Web services. With the QoS, location of users and Web services has been another significant factor in predicting the QoS values. The more factors that are available to the service providers, the more accurate predictions can be generated. However these factors are privacy sensitive and therefore it is risky to disclose them to any third party service provider. To address this challenge, in this paper we develop a privacy preserving protocol to predict missing QoS values and thereby providing Web service recommendations based on past QoS experiences and locations of users. Our protocol is able to achieve user privacy by means of encrypting the QoS and location as well as to select suitable Web services for users without disclosing any private information. We conduct extensive experimental analysis on publicly available data sets and prove that our method is both secure and practical.

In this paper we consider the problem where a client wishes to subscribe to some product or service provided by a server, but maintain their anonymity. At the same time, the server must be able to authenticate the client as a genuine user and be able to discontinue (or revoke) the client's access if the subscription fees are not paid. Current solutions for this problem are typically constructed using some combination of blind signature or zero-knowledge proof techniques, which do not directly support client revocation (that is, revoking a user before expiry of their secret value). In this paper, we present a solution for this problem on the basis of the broadcast encryption scheme, suggested by Boneh et al., by which the server can broadcast a secret to a group of legitimate clients. Our solution allows the registered client to log into the server anonymously and also supports client revocation by the server. Our solution can be used in many applications, such as location-based queries. We formally define a model for our anonymous subscription protocol and prove the security of our solution under this model. In addition, we present experimental results from an implementation of our protocol. These experimental results demonstrate that our protocol is practical.

In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k-means algorithm and multibranch tree structure and propose the α-filtering tree search scheme based on bisecting k-means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filtering the nonrelevant document cluster by calculating the relevance score between the filtering vector and the query vector. The α-filtering tree can improve the efficiency without the loss of search accuracy. The experiment on a real-world dataset demonstrates the effectiveness of our scheme.

With cloud computing becoming increasingly popular, there has been a rapid increase in the number of data owners who outsource their data to the cloud while allowing users to retrieve the data. To preserve the privacy of data, data owners usually encrypt their data before outsourcing them to the cloud, and cloud servers can search across the ciphertext domain on behalf of users without learning any information about the data. However, existing work in the literature mostly supports only a single-user or single-keyword search which is not able to satisfy more desired expressive search. Thus, we propose a searchable encryption primitive with attribute-based access control for hybrid boolean keyword search over outsourced encrypted data. There exist several desirable features: (1) Data owners can set search permissions for outsourced encrypted data according to an access control policy. (2) Multiple users, whose attributes satisfy the access control policy, are allowed to perform a retrieval operation upon the encrypted data. (3) Authorized users are able to perform more expressive search, such as any required boolean keyword expression search. Additionally, this primitive is provably secure under our security model and we have also implemented the prototype to show the practicality of the primitive.

The concept of functional encryption (FE) has been introduced to address the shortcomings of public-key encryption (PKE) in many emerging applications which require both data storage and data sharing (e.g., cloud storage service). One of the major issues existing in most FE schemes is the efficiency, as they are built from bilinear pairings of which the computation is very expensive. A widely accepted solution to this problem is outsourcing the heavy workloads to a powerful third party and leaving the user with the light computation. Nevertheless, it is impractical to assume that the third party (e.g., the cloud) will provide free services. To our knowledge, no attention has been paid to the payment procedure between the user and the third party in an FE with outsourced decryption (FEOD) scheme under the assumption that neither of them should be trusted. Leveraging the transactions on cryptocurrencies supported by the blockchain technology, in this paper, we aim to design FE with payable outsourced decryption (FEPOD) schemes. The payment in an FEPOD scheme is achieved through a blockchain-based cryptocurrency, which enables the user to pay a third party when it correctly completes the outsourced decryption. We define the adversarial model for FEPOD schemes, and then present a generic construction of FEPOD schemes. Also, we evaluate the performance of the proposed generic construction by implementing a concrete FEPOD scheme over a blockchain platform.

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which resulted in a disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. In this paper, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical.

Expectation maximization (EM) is a clustering-based machine learning algorithm that is widely used in many areas of science (e.g., bioinformatics and computer vision) to find maximum likelihood and maximum a posteriori estimates for models with latent variables. To deploy such an algorithm in cloud environments, security and privacy issues need be considered to avoid data breaches or abuses by external malicious parties or even by cloud service providers. However, the processing performance of the EM algorithm poses a challenge in terms of building a secure environment. This article describes an innovative and practical privacy-preserving EM algorithm for cloud systems that addresses this challenge, and estimates the EM parameters in an accurate and secure manner. Fully homomorphic encryption (FHE) is used to ensure the privacy of both the EM algorithm computations and the users' sensitive data in the cloud. A distributed-based approach is also proposed to overcome the overheads of FHE computations and ensure a fast convergence of the EM algorithm. The conducted experiments demonstrate a significant improvement in the convergence time of the distributed EM algorithm, while achieving a high level of accuracy and reducing the associated computational FHE overheads.

In this paper, we consider a scenario where a bitcoin liquidity provider sells bitcoins to clients. When a client pays for a bitcoin online, the provider is able to link the client's payment information to the bitcoin sold to that client. To address the privacy concerns of clients, we require that the bitcoin provider cannot tell the relationship between the real identities of clients and the sold bitcoins in the blockchain. This requirement can be effectively achieved by using blind signatures. However, existing blind signature schemes are incompatible with the Elliptic Curve Digital Signature Algorithm (ECDSA) which is used by most of the existing bitcoin protocol, thus cannot be applied directly in Bitcoin. In this paper, we propose a new blind signature scheme that allows generating a blind signature compatible with the standard ECDSA. Afterwards, we make use of the new scheme to achieve bitcoin transaction anonymity. The new scheme is built on a variant of the Paillier cryptosystem and its homomorphic properties. As long as the modified Paillier cryptosystem is semantically secure, the new blind signature scheme has blindness and unforgeability.

WSN-as-a-Service (WaaS) is a novel application model of wireless sensor networks (WSNs). Owners of WSNs provide data queries as services, while users pay for needed services as they use such services. The adoption of WaaS improves the usage of WSNs and reduces the cost of network deployment and maintenance. It is challenging to protect data from curious users while, at the same time, providing MAX/MIN query services. In this paper, we propose a privacy-preserving MAX/MIN query processing method for WaaS. To the best of our knowledge, this work is the first to discuss a privacy-preserving data query method in the WaaS environment. To implement privacy-preserving MAX/MIN queries, we propose a novel query protocol by adopting the idea of secure multiparty computation. The protocol consists of two cooperative query processing algorithms that are deployed in the aggregate sensor and normal sensors. During query processing, multiple rounds of secure interactions between sensors are performed. In each round, one bit of the query result is determined through cooperation of sensors, while the data of sensors participating in query processing remain private. Curious users cannot obtain any private data from the network even if a few compromised sensors collude with them. The analysis and evaluations indicate that the proposed protocol computes query results reliably, avoids the energy hole problem and is efficient in terms of communication cost.

Summary Anonymous authentication technique receives wide attention in recent years since it can protect users' privacy. Anonymous web browsing refers to utilization of the World Wide Web that hides a user's personally identifiable information from the websites visited. Even if a user can hide the IP address and other physical information with anonymity programs such as Tor, the web server can always monitor the user on the basis of the identity. In this paper, we firstly give an overview and cryptanalysis on the protocol of Yang et al and point out the security weaknesses of their protocol. Then, we propose a new authentication protocol for anonymous web browsing. In the proposed protocol, we take the advantages of a pseudo identity mechanism and an identity‐based elliptic curve cryptography algorithm to achieve user anonymity, robust security, and high efficiency. The result of security analysis and performance evaluation indicate the feasibility and practicality of our proposed anonymous authentication protocol.

The database-driven cognitive radio networks (CRNs) are regarded as a promising approach to utilizing limited spectrum resources in large-scale Internet of Things (IoT). However, database-driven CRNs face some security and privacy threats. Firstly, secondary users (SUs) should send identity and location information to the database (DB) to obtain a list of available channels, such that the curious DB might easily misuse and threaten the privacy of SUs. Secondly, malicious SUs might send fake location information to the DB in order to occupy channels with better quantity in advance and so gain benefits. This might also cause serious interference to primary users (PUs). In this paper, we propose a lightweight privacy-preserving location verification protocol to protect the identity and location privacy of each SU and to verify the location of SUs. In the proposed protocol, the SU does not need to provide location information to request an available channel from the DB. Therefore, the DB cannot get the location information of any SU. In the proposed protocol, the base station (BS) selects some SUs as witnesses to generate location proofs for each other in a distributed fashion. This new witness selection mechanism makes the proposed protocol reliable when a malicious SU generates fake location information to cheat the BS and also prevents SU-Witness collusion attacks. The results also show that the proposed protocol can provide strong privacy preservation for SUs and can effectively verify the location of the SUs. The security analysis shows that the proposed protocol can resist various types of attacks. Moreover, compared with previous protocols, the proposed protocol is lightweight because it relies on symmetric cryptography and it is unaffected by the area covered by the DB.

The Internet of Things (IoT) represents a technology revolution transforming the current environment into a ubiquitous world, whereby everything that benefits from being connected will be connected. Despite the benefits, the privacy of these things becomes a great concern and therefore it is imperative to apply privacy preservation techniques to IoT data collection. One such technique is called data obfuscation in which data is deliberately modified to blur the sensitive information, while preserving the data utility. The current obfuscation techniques, however, focus on the privacy of published datasets shared with untrusted parties. The high connectivity and distributed nature of IoT, opens up the possibility of privacy compromise before obfuscation can take effect, and therefore privacy enforcement should be deployed at earlier stages. Additionally, classical privacy treatments are too restrictive for IoT, where coarser/finer data details should be revealed for different applications. Motivated by these challenges, we propose a framework for privacy preservation in IoT environments that is capable of multi-granular obfuscation by enforcing context-driven disclosure policies. Then, we customize our framework for a smart vehicle system and make use of data stream watermarking techniques to protect privacy at different stages of the data lifecycle. To address possible concerns about additional performance overhead, we show the burden to be very lightweight, thus validating the suitability of ubiquitous use of our framework for IoT settings.

An online estimation algorithm of State-of-Charge (SoC) of Lithium-ion (Li-ion) batteries based on terminal sliding mode (TSM) observer technique is proposed. A first-order RC equivalent circuit model is utilized to describe the dynamical behaviors of Li-ion batteries. A sliding mode observer is developed to track the states of the Li-ion batteries and the control signal of the observer is used to estimate the SoC of a Li-ion battery accurately. The proposed observer is robust to internal parameter uncertainties of the battery model, and the environment changes. Compared with the traditional sliding mode observers, the proposed sliding mode observer has the continuous control signal, which can be used for the SoC estimation algorithm directly. The proposed method has been verified by the estimation results and the effectiveness has benn demonstrated.

In Spatial Crowdsourcing (SC), a set of spatio-temporal tasks are outsourced to a set of workers, i.e., individuals with mobile devices who physically travel to task locations. The process of matching workers to tasks is performed by a SC server. To perform matching, the SC server needs access to worker locations. However, the SC server may not be trustworthy. Current solutions for protecting locations of workers assume that a trusted cellular service provider (CSP) knows the identities and locations of workers and sanitizes locations before sharing them with the SC server. In practice, the CSP may not have the technical ability, nor the proper incentives to perform the sanitization task. Thus, location protection must be performed by a Location Privacy Provider (LPP). To prevent identity disclosure to the LPP, we propose a novel solution based on anonymous credentials which preserves worker privacy. Our solution allows registered workers to log on to the LPP and receive tasks from the SC-server anonymously. In addition, our solution assures the confidentiality and integrity of spatial tasks. Our implementation and experiments demonstrate that our solution is practical.

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

The industrial Internet-of-things (IIoT) can be seen as the usage of Internet-of-things technologies in industries, which provides a way to improve the operational efficiency. An attribute-based signature (ABS) has been a very useful technique for services requiring anonymous authentication in practice, where a signer can sign a message over a set of attributes without disclosing any information about his/her identity, and a signature only attests to the fact that it is created by a signer with several attributes satisfying some claim predicate. However, an ABS scheme requires exponentiation and/or pairing operations in the signature generation and verification algorithms, and hence, it is quite expensive for resource-constrained devices like a sensor in the IIoT network to run an ABS scheme. To reduce the computational overheads for both signers and verifiers, it has been suggested to introduce a server to help with signature generation and verification, but existing results on the ABS with “server-aided computation” either suffer from the security issues or are not sufficiently efficient. In this paper, we consider server-aided ABS one step further, and propose a notion called server-aided ABS with revocation (SA-ABSR), which not only securely mitigates the workloads of users in generating and verifying signatures, but also enables user revocation by having the server immediately stop signature generations for revoked signers. We formally define the security model for SA-ABSR, present a concrete construction of SA-ABSR based on a standard ABS scheme, and prove its security under the defined security model. Also, we implement the proposed SA-ABSR scheme and the underlying standard ABS scheme to evaluate the performance, from which it is easy to see that the proposed SA-ABSR scheme is more efficient than its underlying ABS scheme.

There are many application domains, where hierarchical data is inherent, but surprisingly, there are few techniques for mining patterns from such important data. Hierarchical Heavy Hitters (HHH) and multilevel and Cross-Level Association Rules (CLAR) mining are well-known hierarchical pattern mining techniques. The problem in these techniques; however, is that they focus on capturing only global patterns from data but cannot identify local contextual patterns. Another problem in these techniques is that they treat all data items in the transaction equally and do not consider the sequential nature of the relationship among items within a transaction; hence, they cannot capture the correlation semantic within the transactions of the data items. There are many applications such as clickstream mining, healthcare data mining, network monitoring, and recommender systems, which require to identify local contextual patterns and correlation semantics. In this work, we introduce a new concept, which can capture the sequential nature of the relationship between pairs of hierarchical items at multiple concept levels and can capture local contextual patterns within the context of the global patterns. We call this notion Hierarchically Correlated Heavy Hitters (HCHH). Specifically, the proposed approach finds the correlation between items corresponding to hierarchically discounted frequency counts. We have provided formal definitions of the proposed concept and developed algorithmic approaches for computing HCHH in data streams efficiently. The proposed HCHH algorithm have deterministic error guarantees, and space bounds. It requires O(η/ϵpϵs) memory, where h is a small constant, and ϵp∈ [0,1], ϵs∈ [0,1] are user defined parameters on upper bounds of estimation error. We have compared the proposed HCHH concept with existing hierarchical pattern mining approaches both theoretically as well as experimentally.

With the rapid development of the social networks, Collaborative Filtering (CF)-based recommender systems have been increasingly prevalent and become widely accepted by users. The CF-based techniques generate recommendations by collecting privacy sensitive data from users. Usually, the users are sensitive to disclosure of personal information and, consequently, there are unavoidable security concerns since private information can be easily misused by malicious third parties. In order to protect against breaches of personal information, it is necessary to obfuscate user information by means of an efficient encryption technique while simultaneously generating the recommendation by making true information inaccessible to service providers. Therefore, we propose a privacy preserving user-based CF technique based on homomorphic encryption, which is capable of determining similarities among users followed by generating recommendations without revealing any private information. We introduce different semi-honest parties to preserve privacy and to carry out intermediate computations for generating recommendations. We implement our method on publicly available datasets and show that our method is practical as well as achieves high level of security for users without compromising the recommendation accuracy.

With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to the fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is exposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware failure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate verifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering, which is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which is based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based on the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and thus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query vector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the proposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to find out some users whose profiles are similar to the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online data site, Ashley Madison, was hacked, which results in disclosure of a large number of dating user profiles. This serious data breach has urged researchers to explore practical privacy protection for user profiles in online dating. In this paper, we give a privacy-preserving solution for user profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out some matching users with the help of the multiple servers without revealing to anyone privacy of the query and the queried user profiles. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our implementation and experiments demonstrate that our solution is practical.

Summary Data outsourcing is a major component for cloud computing because data owners are able to distribute resources to external services for sharing with users and organizations. A crucial problem for owners is how to secure sensitive information accessed by legitimate users only using the trusted services. We address the problem with access control methods to enforce selective access to outsourced data without involving the owner in authorization. The basic idea is to combine cryptography with authorizations, and data owners assign keys to roles that will enforce access via encryption. A formal access model is designed to analyze the translating an authorization policy into an equivalent encryption policy. The paper also investigates the effect of role hierarchy structure in the authorization process. The role‐based access management methods are implemented withXACMLby usingWSOIdentity Server. The comparisons with other related work are presented. Finally, the future work is introduced.Copyright © 2014 John Wiley & Sons, Ltd.

The main goal of a personalized recommender system is to provide useful recommendations on various items to the users. In order to generate recommendations, the service needs to access various types of user data such as previous product purchasing history, demographic and biographical information. However, users are sensitive to disclosure of personal information as it can be easily misused by malicious third parties. Consequently, there are unavoidable security concerns which will become known through attempted unauthorized access while providing the recommendation services. In order to protect against breaches of personal information, it is necessary to obfuscate the user information by means of an efficient encryption technique while simultaneously generating the recommendation by making true information inaccessible to the system. To address these challenges, we propose a privacy-preserving recommender system using homomorphic encryption, by which the system can provide recommendations without knowing the actual ratings. Our approach is based on the ElGamal cryptosystem by which both addition and multiplication of plaintexts can be performed. The performance of the proposed scheme shows significantly high accuracy in-terms of computation and communication costs as well as outperforming other existing solutions.

In a two-server password-authenticated key exchange (PAKE) protocol, a client splits its password and stores two shares of its password in the two servers, respectively, and the two servers then cooperate to authenticate the client without knowing the password of the client. In case one server is compromised by an adversary, the password of the client is required to remain secure. In this paper, we present two compilers that transform any two-party PAKE protocol to a two-server PAKE protocol on the basis of the identity-based cryptography, called ID2S PAKE protocol. By the compilers, we can construct ID2S PAKE protocols which achieve implicit authentication. As long as the underlying two-party PAKE protocol and identity-based encryption or signature scheme have provable security without random oracles, the ID2S PAKE protocols constructed by the compilers can be proven to be secure without random oracles. Compared with the Katz et al.'s two-server PAKE protocol with provable security without random oracles, our ID2S PAKE protocol can save from 22 to 66 percent of computation in each server.

In recent years, wireless sensor networks have been widely used in healthcare applications, such as hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data during transmission, but cannot stop the inside attack where the administrator of the patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient data. The main contribution of this paper is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients' privacy.

Publicly accessible data warehouses are an indispensable resource for data analysis. However, they also pose a significant risk to the privacy of the clients, since a data warehouse operator may follow the client's queries and infer what the client is interested in. Private information retrieval (PIR) techniques allow the client to retrieve a cell from a data warehouse without revealing to the operator which cell is retrieved and, therefore, protects the privacy of the client's queries. However, PIR cannot be used to hide online analytical processing (OLAP) operations performed by the client, which may disclose the client's interest. This paper presents a solution for private cell retrieval from a data warehouse on the basis of the Paillier cryptosystem. By our solution, the client can privately perform OLAP operations on the data warehouse and retrieve one (or more) cell without revealing any information about which cell is selected. In addition, we propose a solution for private block download on the basis of the Paillier cryptosystem. Our private block download allows the client to download an encrypted block from a data warehouse without revealing which block in a cloaking region is downloaded and improves the feasibility of our private cell retrieval. Our solutions ensure both the server's privacy and the client's privacy. Our experiments have shown that our solutions are practical.

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, we study approximate k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about approximate k nearest points of interest (POIs) on the basis of his current location. We propose a basic solution and a generic solution for the mobile user to preserve his location and query privacy in approximate kNN queries. The proposed solutions are mainly built on the Paillier public-key cryptosystem and can provide both location and query privacy. To preserve query privacy, our basic solution allows the mobile user to retrieve one type of POIs, for example, approximate k nearest car parks, without revealing to the LBS provider what type of points is retrieved. Our generic solution can be applied to multiple discrete type attributes of private location-based queries. Compared with existing solutions for kNN queries with location privacy, our solution is more efficient. Experiments have shown that our solution is practical for kNN queries.

Recently, the paradigm of data mining-as-a-service in cloud computing environment has been attracting interests. In this paradigm, a company (data owner), lacking data storage, computational resources and expertise, stores its data in the cloud and outsources its mining tasks to the cloud service provider (server). In order to protect the privacy of the outsourced database and the association rules mined, k-anonymity, k-support, and k-privacy techniques have been proposed to perturb the data before it is uploaded to the server. These techniques are computationally expensive. If the data owner has resources to use these techniques, then it is often able to execute association rule mining locally. In this paper, we consider a scenario where a user (data owner) encrypts its data and stores it in the cloud. To mine association rules from its data, the user outsources the task to n (≥ 2) "semi-honest" servers, which cooperate to perform association rule mining on the encrypted data in the cloud and return encrypted association rules to the user. In this setting, we provide three solutions to protecting data privacy during association rule mining. Our solutions are built on the distributed ElGamal cryptosystem and achieve item privacy, transaction privacy and database privacy, respectively, as long as at least one out of the n servers is honest. To reduce the possibility that all servers are compromised, the user can use servers from different cloud providers. Our implementation and experiments demonstrate that our solutions are practical.

Threshold password-authenticated secret sharing (TPASS) protocols allow a client to secret-share a secret s among n servers and protect it with a password $$\mathsf {pw}$$ , so that the client can later recover s from any subset of t of the servers using the password $$\mathsf {pw}$$ , but so that no coalition smaller than t learns anything about s or can mount an offline dictionary attack on the password $$\mathsf {pw}$$ . Some TPASS protocols have appeared in the literature recently. The protocol by Bagherzandi et al. (CCS 2011) leaks the password if a client mistakenly executes the protocol with malicious servers. The first t-out-of-n TPASS protocol for any $$n>t$$ that does not suffer from this shortcoming was given by Camenisch et al. (CRYPTO 2014). This protocol, proved to be secure in the UC framework, requires the client to involve in many communication rounds so that it becomes impractical for the client. In this paper, we present a practical TPASS protocol which is in particular efficient for the client, who only needs to send a request and receive a response. In addition, we have provided a rigorous proof of security for our protocol in the standard model.

The shortest path routing (SPR) problem is a well-known challenge in the field of mobile network routing. The aim is to find the least cost path that connect a specific source node with a specific destination node. Although there are numerous algorithms to solve SPR, most of them consider only static environments in which the network topology and link-cost never change. A network with dynamic topologies and cost are indeed more challenging but more practical in real world applications. This paper presents a memetic algorithm for dynamic SPR (DSPR) problems in a mobile network. The proposed approach consists of three stages: genetic algorithm, local search and elitism-based immigrants procedure. Genetic algorithm (GA) is applied in the first stage to explore the search space and generate a new set of solutions. The generated solutions are further improved in the second stage by a local search algorithm. In third stage, an elitism-based immigrants procedure is activated to handle the dynamic changes by maintaining the diversity of the search process. The performance of the proposed algorithm has been evaluated on dynamic shortest path routing problem instances under both cyclic and acyclic environments. The study shows that, on both circumstances, the proposed algorithm is very stable with regards to dynamic network changes. This method is highly competitive compared to state-of-the-art algorithms in the literature as it outperformed these algorithms on all instances of dynamic routing during evaluation.

It is attractive for an organization to outsource its data analytics to a service provider who has powerful platforms and advanced analytics skills. However, the organization (data owner) may have concerns about the privacy of its data. In this paper, we present a method that allows the data owner to encrypt its data with a homomorphic encryption scheme and the service provider to perform k-means clustering directly over the encrypted data. However, since the ciphertexts resulting from homomorphic encryption do not preserve the order of distances between data objects and cluster centers, we propose an approach that enables the service provider to compare encrypted distances with the trapdoor information provided by the data owner. The efficiency of our method is validated by extensive experimental evaluation.

Motivated by applications in social networks, a new type of dominating set named Positive Influence Dominating Set (PIDS) has been studied in the literature. In this paper, we investigate cooperative cost games arising from PIDS problem on social network graphs. We propose two new game models, Rigid PIDS Game and Relaxed PIDS Game, and focus on their cores. First, a relationship between the cores of both games is obtained. Next, we also prove that the core of the relaxed PIDS game is nonempty if and only if there is no integrality gap for the relaxation linear programming of the PIDS problem on graph G.

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, we study k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about k nearest points of interest (POIs) on the basis of his current location. We propose a solution for the mobile user to preserve his location privacy in kNN queries. The proposed solution is built on the Paillier public-key cryptosystem and can provide both location privacy and data privacy. In particular, our solution allows the mobile user to retrieve one type of POIs, for example, k nearest car parks, without revealing to the LBS provider what type of points is retrieved. For a cloaking region with n×n cells and m types of points, the total communication complexity for the mobile user to retrieve a type of k nearest POIs is O(n+m) while the computation complexities of the mobile user and the LBS provider are O(n + m) and O(n2m), respectively. Compared with existing solutions for kNN queries with location privacy, our solutions are more efficient. Experiments have shown that our solutions are practical for kNN queries.

Private searching on streaming data is a process to dispatch to a public server a program, which searches streaming sources of data without revealing searching criteria and then sends back a buffer containing the findings. From an Abelian group homomorphic encryption, the searching criteria can be constructed by only simple combinations of keywords, for example, disjunction of keywords. The recent breakthrough in fully homomorphic encryption has allowed us to construct arbitrary searching criteria theoretically. In this paper, we consider a new private query, which searches for documents from streaming data on the basis of keyword frequency, such that the frequency of a keyword is required to be higher or lower than a given threshold. This form of query can help us in finding more relevant documents. Based on the state of the art fully homomorphic encryption techniques, we give disjunctive, conjunctive, and complement constructions for private threshold queries based on keyword frequency. Combining the basic constructions, we further present a generic construction for arbitrary private threshold queries based on keyword frequency. Our protocols are semantically secure as long as the underlying fully homomorphic encryption scheme is semantically secure.

In this paper we present a solution to one of the location-based query problems. This problem is defined as follows: (i) a user wants to query a database of location data, known as Points Of Interest (POIs), and does not want to reveal his/her location to the server due to privacy concerns; (ii) the owner of the location data, that is, the location server, does not want to simply distribute its data to all users. The location server desires to have some control over its data, since the data is its asset. We propose a major enhancement upon previous solutions by introducing a two stage approach, where the first step is based on Oblivious Transfer and the second step is based on Private Information Retrieval, to achieve a secure solution for both parties. The solution we present is efficient and practical in many scenarios. We implement our solution on a desktop machine and a mobile device to assess the efficiency of our protocol. We also introduce a security model and analyse the security in the context of our protocol. Finally, we highlight a security weakness of our previous work and present a solution to overcome it.

Recently, the amount of personal medical information online is increasing exponentially, opening up new avenues for hackers to expose personal data that, unlike financial information, can result in a permanent violation of privacy. To protect the privacy of patient data, such as electronic health records (EHRs), access control was used before and attributed-based encryption is used recently. These techniques can effectively prevent from the outside attacks, but are hard to withstand the inside attacks, where the database administrator or the key manager is an attacker. In this paper, we provide a solution to protect the privacy of patient data (EHRs) under the multi-party framework where all EHRs are encrypted with the common public key and an encrypted EHR can be decrypted only by the cooperation of all parties. Based on the ElGamal threshold public key encryption scheme, we propose several EHR access control protocols where multiple parties cooperate to control clinicians' access to EHRs without actually knowing EHRs. Our solution can protect the patient data against the inside attacks as long as at least one party can be trusted. Because our solution is built on Public Key Infrastructure (PKI), it facilitates the clinician registration and revocation.

Publicly accessible data warehouses are an indispensable resource for data analysis. But they also pose a significant risk to the privacy of the clients, since a data warehouse operator may follow the client's queries and infer what the client is interested in. Private Information Retrieval (PIR) techniques allow the client to retrieve a cell from a data warehouse without revealing to the operator which cell is retrieved. However, PIR cannot be used to hide OLAP operations performed by the client, which may disclose the client's interest. This paper presents a solution for private data warehouse queries on the basis of the Boneh-Goh-Nissim cryptosystem which allows one to evaluate any multi-variate polynomial of total degree 2 on ciphertexts. By our solution, the client can perform OLAP operations on the data warehouse and retrieve one (or more) cell without revealing any information about which cell is selected. Furthermore, our solution supports some types of statistical analysis on data warehouse, such as regression and variance analysis, without revealing the client's interest. Our solution ensures both the server's security and the client's security.

Private Information Retrieval (PIR) allows a user to retrieve the ith bit of an n-bit database without revealing to the database server the value of i. In this paper, we present a PIR protocol with the communication complexity of O(γ logn) bits, where -y is the ciphertext size. Furthermore, we extend the PIR protocol to a private block retrieval (PBR) protocol, a natural and more practical extension of PIR in which the user retrieves a block of bits, instead of retrieving single bit. Our protocols are built on the state-of-the-art fully homomorphic encryption (FHE) techniques and provide privacy for the user if the underlying FHE scheme is semantically secure. The total communication complexity of our PBR is O(γ logm + γn/m) bits, where m is the number of blocks. The total computation complexity of our PBR is O(m logm) modular multiplications plus O(n=2) modular additions. In terms of total protocol execution time, our PBR protocol is more efficient than existing PBR protocols which usually require to compute O(n=2) modular multiplications when the size of a block in the database is large and a high-speed network is available.

Password-authenticated key exchange (PAKE) is where a client and a server, who share a password, authenticate each other and meanwhile establish a cryptographic key by exchange of messages. In this setting, all the passwords necessary to authenticate clients are stored in a single server. If the server is compromised, due to, for example, hacking or even insider attack, passwords stored in the server are all disclosed. In this paper, we consider a scenario where two servers cooperate to authenticate a client and if one server is compromised, the attacker still cannot pretend to be the client with the information from the compromised server. Current solutions for two-server PAKE are either symmetric in the sense that two peer servers equally contribute to the authentication or asymmetric in the sense that one server authenticates the client with the help of another server. This paper presents a symmetric solution for two-server PAKE, where the client can establish different cryptographic keys with the two servers, respectively. Our protocol runs in parallel and is more efficient than existing symmetric two-server PAKE protocol, and even more efficient than existing asymmetric two-server PAKE protocols in terms of parallel computation.

In this paper we present a solution to one of the location-based query problems. This problem is defined as follows: (i) a user wants to query a database of location data, known as Points Of Interest (POI), and does not want to reveal his/her location to the server due to privacy concerns, (ii) the owner of the location data, that is, the location server, does not want to simply distribute its data to all users. The location server desires to have some control over its data, since the data is its asset. Previous solutions have used a trusted anonymiser to address privacy, but introduced the impracticality of trusting a third party. More recent solutions have used homomorphic encryption to remove this weakness. Briefly, the user submits his/her encrypted coordinates to the server and the server would determine the user's location homomorphically, and then the user would acquire the corresponding record using Private Information Retrieval techniques. We propose a major enhancement upon this result by introducing a similar two stage approach, where the homomorphic comparison step is replaced with Oblivious Transfer to achieve a more secure solution for both parties. The solution we present is efficient and practical in many scenarios. We also include the results of a working prototype to illustrate the efficiency of our protocol.

High dimensional clustering is often encountered in real application and projective clustering is an effective way to deal with high dimensional clustering problems aiming to capture the dense areas embedded in subsets of attributes/subspaces. Most projective clustering algorithms use equal or varying width hyper-rectangle structure to identify the dense areas and their locations. Therefore, it is a crucial task to decide the widths of these hyper-rectangle structures in projective clustering. Naturally, making use of the real data distribution directly to determine the widths of the dense structures is a promising and feasible approach. In this paper, we propose a projective clustering algorithm based on hyper-rectangle structure, whose width is estimated from the kernel distribution of real data. In particular, we first define a structure called Significant Local Dense Area (SLDA) structure by using an efficient kernel density estimator, Rodeo; and then design a greedy search method to find the whole SLDAs covered the data distribution in the high-dimensional space; eventually, we run a single-linkage clustering algorithm on the SLDAs to form the final clusters and identify the outliers. The main strength of the proposed algorithm is validated by the experiments on synthetic and real world data sets.

Recently, Bertino, Shang and Wagstaff proposed a time-bound hierarchical key management scheme for secure broadcasting. Their scheme is built on elliptic curve cryptography and implemented with tamper-resistant devices. In this paper, we present two collusion attacks on Bertino-Shang-Wagstaff scheme. The first attack does not need to compromise any decryption device, while the second attack requires to compromise single decryption device only. Both attacks are feasible and effective.

Frequent Path tree (FP-tree) is a popular method to compute association rules and is faster than Apriori-based solutions in some cases. Association rule mining using FP-tree method cannot ensure entire privacy since frequency of the itemsets are required to share among participants at the first stage. Moreover, FP-tree method requires two scans of database transactions which may not be the best solution if the database is very large or the database server does not allow multiple scans. In addition, one-pass FP-tree can accommodate continuous or periodically changing databases without restarting the process as opposed to a regular FP-tree based solution. In this paper, the authors propose a one-pass FP-tree method to perform association rule mining without compromising any data privacy among two parties. A fully homomorphic encryption system over integer numbers is applied to ensure secure computation among two data sites without disclosing any number belongs to themselves.

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analyses of Web usage data will lead to discovering Web user access patterns, and in turn, facilitating users to locate more preferable Web contents via c

In this paper, a certificateless proxy signature scheme is introduced. The advantage of our scheme is that it can be extended into a certificateless proxy blind signature scheme very easily.

Typical protocols for password-based authentication assumes a single server which stores all the passwords necessary to authenticate users. If the server is compromised, user passwords are disclosed. To address this issue, Yang et al. proposed a practical password-based two-server authentication and key exchange protocol, where a front-end server, keeping one share of a password, and a back-end server, holding another share of the password, cooperate in authenticating a user and, meanwhile, establishing a secret key with the user. In this paper, we present two ``half-online and half-offline'' attacks to Yang et al.'s protocol. By these attacks, user passwords can be determined once the back-end server is compromised. Therefore, Yang et al.'s protocol has no essential difference from a password-based single-server authentication protocol.

Confidentiality and authentication are crucial to wireless sensor networks when sensors are deployed in hostile environments, e.g., battle fields and antiterrorist operations. In this paper, we come up with (1) a group key distribution protocol for a base station to distribute a group key to sensors and (2) a broadcast authentication protocol for sensors to authenticate messages broadcasted by a base station. Security and performance analysis show that both protocols are secure and efficient. In addition, our group key distribution protocol supports dynamic wireless sensor network which allows new sensors to be added in and compromised sensors to be forced to leave.

The notion of limited verifier signature (LVS) was first introduced by Araki et al. in 1999. It is a useful cryptographic primitive to limit the publicly verifiable property of ordinary digital signatures. In a LVS, the signature can be verified by a limited verifier. When necessary, the signer or the limited verifier can provide a proof to convince a designated verifier (eg., a judge) that the signer has indeed generated the signature. However, the judge cannot transfer this proof to convince any other party. Also, the LVS should be converted into an ordinary one for public verification if required. In this paper, we propose an efficient LVS scheme which is more efficient than previous proposed schemes. Based on the intractability of the Computational Diffie-Hellman (CDH) problem, we give the security proofs of the scheme in the random oracle model.

Abstract Many commercial applications of broadcast require that only those users who have paid for the service can retrieve broadcast information. This is usually implemented by distributing a broadcast key to the set of privileged users and then encrypting broadcast information with the key. The set of privileged users is dynamic because users may join or quit the service at various times. Current solutions for efficient broadcast key distribution are usually built on the Shamir secret sharing scheme and implemented with smart cards. All such solutions revoke a pre‐specified number of users only. In this paper, we propose for the first time a broadcast key distribution scheme with the capability of revoking any number of users. The scheme is efficient and can be implemented on Smart Cards with low power setting. Furthermore, it provides authentication of the broadcaster to the receiver, authentication of the receiver to the broadcaster, and authentication of the information sent, and allows traitor tracing. Security analysis shows that our scheme is secure against both passive and active attacks. Performance analysis demonstrates that it is more efficient than other schemes designed for similar purposes. Copyright © 2008 John Wiley & Sons, Ltd.

In order to secure communications for dynamic peer groups in mobile computing environments, key agreement protocols are required. In this paper, we propose a new key agreement protocol based on identities of mobile users, composed of a basic protocol and a dynamic protocol, for dynamic peer groups. With the basic protocol, an initial secret group key can be achieved in a peer group. By the dynamic protocol, a new secret group key can be reached whenever member or mass join, group mergence, group division, member or mass quit occurs. Our protocol has security features, such as implicit group key authentication, key confirmation, forward secrecy, key independence, and etc. Because our protocol runs in parallel, it is more efficient than those running in series.

Recent technological advances in mobile communications have allowed public to take part in noncritical elections (such as deciding the most valuable player in sports events) with low-power, low-cost and small sized portable communication devices. However, more critical elections (such as political elections) have not yet held in mobile communication environments, mainly because of security concerns, communication and computation constrains of portable communication devices. In this paper, we come up with a secure electronic election scheme in mobile communication environments, which meets completeness, soundness, privacy, unreusability, eligibility, fairness, and verifiability. Our scheme is based on blind signature and needs low computation complexity in portable communication devices

Recently, Chien proposed a time-bound hierarchical key assignment scheme based on tamper-resistant devices. Without public key cryptography, Chien's scheme greatly reduces computation load and implementation cost. In this paper, we show that Chien's scheme is insecure against a collusion attack whereby three users conspire to access some secret class keys that they should not know according to Chien's scheme.

Lots of conference key agreement protocols have been suggested to secure computer network conference. Most of them operate only when all conferees are honest, but do not work when some conferees are malicious and attempt to delay or destruct the conference. Recently, Tzeng proposed a conference key agreement protocol with fault tolerance in terms that a common secret conference key among honest conferees can be established even if malicious conferees exist. In the case where a conferee can broadcast different messages in different subnetworks, Tzeng's protocol is vulnerable to a "different key attack" from malicious conferees. In addition, Tzeng's protocol requires each conferee to broadcast to the rest of the group and receive n - 1 message in a single round (where n stands for the number of conferees). Moreover, it has to handle n simultaneous broadcasts in one round. In this paper, we propose a fault-tolerant conference key agreement protocol, in which each conferee only needs to send one message to a "semitrusted" conference bridge and receive one broadcast message. Our protocol is an identity-based key agreement, built on elliptic curve cryptography. It is resistant to the different key attack from malicious conferees and needs less communication cost than Tzeng's protocol.

Tzeng (2002) proposed a time-bound cryptographic key assignment scheme for access control in a partial-order hierarchy. In this paper, we show that Tzeng's scheme is insecure against the collusion attack whereby three users conspire to access some secret class keys that they should not know according to Tzeng's scheme.

A well-designed cooperation strategy for a task oriented multiagent team is important as it can improve performance. A challenging research issue in cooperation concerns the extent to which an agent should pay attention to the actions and effects of other agents. In this paper, we address this issue in the context of an object search team. We first propose the concept of an activity window which captures an agent's view of the activities and effects of the team. Then we pinpoint some criteria that can be used to determine whether it is beneficial for an agent to put an action of the team into its window. Finally, we present experimental results to test these criteria.

A growing application area in mobile communications is mobile teleconference in which a group of mobile users collaborate in an interactive procedure, such as a board meeting, a task force, a scientific discussion, or even a virtual classroom. Wireless communications transmit conversations via radio, making them more susceptible to eavesdropping and unauthorized access than are conversations carried via wires. Therefore, it is crucial to ensure confidentiality and authenticity in a mobile teleconference. The authors design a new secure conference scheme for mobile communications. Based on a modular square root technique, this scheme is secure against eavesdropping, impersonating, and tracking attacks and allows a participant to join or quit a mobile teleconference dynamically.

We propose a confidential information gathering system which employs a software agent traversing a list of information resource servers on the Internet to gather confidential information. Both software agent technology and cryptographic technology are applied to automate and secure the confidential information gathering process.

Changeability is one of the major concerns in software development. OO (object-oriented) technology itself is not enough to solve this problem. In this paper, we first discuss how the changeability issue is handled in current software development methods and what is the most important prerequisite for solving the software changeability problem. Based on these analyses, we argue that most of current OO analysis methods have missed an important activity in their analysis phase, i.e. change analysis. Thus, OO software systems developed according to these methods often cannot exhibit the expected maintainability, extensibility and reusability. In order to improve current OO analysis methods, we have proposed a conceptual framework for conducting a systematic change analysis in the software analysis phase. A detailed case study of the application of this framework in a real software project has also been described in this paper.

We propose a secure auction-like negotiation protocol for agent based Internet trading, which not only retains the agent's mobility and flexibility, but also takes secure measures to prevent attacks from malicious hosts during the negotiation process. The particular features of the proposed protocol are: (1) negotiation for agent based trading is performed through a novel pattern of electronic auction; (2) negotiation results between two hosts are ensured to be valid with their signatures; (3) malicious actions can be detected and the breeder can be dug out by the help of sociological factors; (4) information gathering and negotiation processes are combined together while few communications are needed.