This paper investigates covert communication in spectrum-sharing Internet of Things (IoT) device-to-device (D2D) networks assisted by unmanned aerial vehicles equipped with intelligent reflecting surfaces (UAV-IRS). Existing schemes assume known or deterministic eavesdropper locations, which limits their effectiveness in the presence of spatially random adversaries in IoT environments. They also often neglect severe interference in spectrum-sharing environments and lack effective protection mechanisms for cellular links, resulting in degraded covert reliability and cellular performance. To address these challenges, we propose a UAV–IRS-assisted D2D covert communication scheme for spectrum-sharing networks, where a full-duplex base station simultaneously receives cellular signals and generates artificial noise (AN) to combat spatially random eavesdroppers. The spatial uncertainty is characterized by modeling three representative configurations between the eavesdroppers’ surveillance regions and the guard zone of the cellular link, i.e., disjoint, overlapping, and contained. Meanwhile, the average minimum detection error probability is derived as the covertness requirement. A two-layer optimization framework is then designed to jointly optimize D2D and AN transmit powers, UAV location, and IRS phase shifts, while ensuring cellular reliability. Simulation results demonstrate the effectiveness of the proposed scheme in enhancing the covert rate in IoT spectrum-sharing networks.

A decentralized storage network (DSN) collects idle storage resources from Internet nodes for low-cost rental to users, and its scale has grown exponentially. In a DSN, most users rely on a centralized third-party service provider (SP) to process userside data and interact with decentralized storage nodes (SNs), making users suffer from a single point of failure. Additionally, since both SP and SNs may offer malicious services, enabling fault tolerance, data confidentiality, and availability guarantee with public verifiability is crucial in the presence of such threats. In this paper, we propose TolerStore, a completely decentralized service framework for DSNs with decentralized SPs and SNs, which can tolerate Byzantine SPs and malicious SNs. To the best of our knowledge, TolerStore is the first to develop a blockchain with multiple SPs for privacy-aware data processing in DSNs, which is formally proven to ensure Byzantine fault tolerance, data confidentiality, public verification, and data availability. Furthermore, we propose an optimized Byzantine Fault-Tolerant consensus with an adaptive leader rotation, incorporating homomorphic fingerprints to verify privacy-aware data processing with enhanced performance. We implement a TolerStore prototype over Hyperledger Fabric, and extensive experiments show that it tolerates [$\frac{N-1}{3}$] Byzantine SPs and 50% malicious SNs with up to 99.99% data availability.

Nowadays, crowdsourcing has evolved into a cost-efficient and scalable task execution paradigm that benefits both task requesters and workers. Task matching is a crucial crowdsourcing procedure for deciding the task execution quality, but security and privacy concerns arise as the crowdsourcing platform cannot be fully trusted. Existing privacy-aware task-matching schemes are limited to intra-platform central matching in the semi-honest model and coarse-grained keyword/location-based matching over one single attribute. Solutions supporting secure cross-platform and fine-grained task matching in the malicious model are urgently needed. In this paper, we first formally defined BFCrowd, a federated crowdsourcing system built on a consortium blockchain. BFCrowd aggregates multi-platform resources and enables decentralized and reliable cross-platform task matching using smart contracts, in the presence of malicious workers and platforms. Notably, we design a fully secure ciphertext-policy attribute-based encryption scheme with concealed access policies and user-side lightweight decryption, which thoroughly caters to the dual-side privacy demand and resource-limited workers and serves for fine-grained expressive task matching over multiple attributes. Moreover, it supports comparison over numerical attributes. Formal security analysis proves the desirable privacy guarantees in the standard model and collusion resistance. Extensive experiments implemented atop Hyperledger Fabric demonstrate both on-chain and off-chain performance.

The proliferation of mobile internet and sharing economy has catalyzed the emergence of Ride-Sharing Services (RSSs) as a paradigm of spatial crowdsourcing in intelligent transportation. Compared with ride-hailing, RSSs present heightened challenges in security and service quality management due to bidirectional disclosure of trip plans and complex matching logic. Existing secure RSS solutions predominantly operate under semi-honest threat models or suffer from prohibitive computational complexity in service composition. However, ensuring public verifiability of matching outcomes is equally critical to prevent manipulation and ensure accountability in decentralized environments. Moreover, achieving a harmonious trade-off among privacy preservation, public verifiability, and matching efficiency remains an open challenge in RSS systems. This work proposes TBRS, a novelTEE-Blockchain powered privacy-awareRide-Sharing framework, which innovatively addresses three core challenges in service computing: (1) formalizing aninclusive matching modelthat extends traditional identical matching through trajectory region overlap analysis and direction alignment verification; (2) designing anIndex-Preserving Bloom Filter (IP-BF)coupled with Hilbert R-tree spatial indexing, achieving$O(\log n)$matching complexity through computational geometry optimization; (3) implementing a hybrid trusted execution environment via SGX-enhanced consortium blockchain withprivate smart contracts, ensuring verifiable service operations management under malicious threats. The framework demonstrates significant advancements in service performance management through systematic experiments: 2${\times }$$\sim$11${\times }$acceleration in on-chain service composition, 6×$\sim$33× improvement in off-chain computation efficiency, while maintaining over 99% service matching accuracy. These results signify that TBRS effectively breaks the efficiency bottleneck of existing privacy-preserving RSS solutions, making decentralized ride-sharing practical for deployment.

The integration of blockchain technology into healthcare presents a paradigm shift for secure data management, enabling decentralized and tamper-proof storage and sharing of sensitive Electronic Health Records (EHRs). However, existing blockchain-based healthcare systems, while providing robust access control, commonly overlook the high latency in user-side re-computation of hashes for integrity verification of large multimedia data, impairing their practicality, especially in time-sensitive clinical scenarios. In this paper, we propose FAITH, an innovative scheme for Fast Authenticated and Interoperable11We focus on the secure data sharing across healthcare institutions. mulTimedia Healthcare data storage and sharing over hybrid-storage blockchains. Rather than user-side hash re-computations, FAITH lets an off-chain storage provider generate verifiable proofs using recursive Zero-Knowledge Proofs (ZKPs), while the user only needs to perform lightweight verification. For flexible access authorization, we leverage Proxy Re-Encryption (PRE) and enable the provider to conduct ciphertext re-encryption, in which the re-encryption correctness can be verified via ZKPs against the malicious provider. All metadata and proofs are recorded on-chain for public verification. We provide a comprehensive analysis of FAITH's security regarding data privacy and integrity. We implemented a prototype of FAITH, and extensive experiments demonstrated its practicality for time-critical healthcare applications, dramatically reducing user-side verification latency by up to 98%, bringing it from 4 s down to around 70 ms for a 5 GB encrypted file.

This article proposes an energy-efficient covert offloading scheme for blockchain-enabled Internet of Things (IoT), allowing sensors to upload tasks undetected by adversaries while ensuring satisfaction in paid computation offloading. Covert communication conceals the existence of transmitted signals or links. However, existing schemes primarily rely on artificial noise (AN) or wireless channel uncertainty, resulting in low covert rates for IoT offloading scenarios. Additionally, blockchain-enabled IoT, being value-oriented, necessitates consideration of sensors’ satisfaction during covert offloading. To tackle these challenges, the proposed scheme combines the adversary’s channel estimation errors with AN to enhance the covert rate, while also matching sensors’ satisfaction with the computation resources of mobile edge servers. Notably, a closed-form expression of the average minimum error detection probability is derived to maximize the effective covert rate. Furthermore, an integrated algorithm combining the Kuhn-Munkres (KM) algorithm with two bubble sort algorithms is designed to minimize energy consumption. Both analytical and simulation results demonstrate that the proposed scheme significantly reduces energy consumption compared to existing solutions.

In enterprize-scale Internet of Things, users need to query data by accessing the resource-constrained smart nodes. Such queries typically include data from one node (DON), and data from one catalog of multiple nodes (DOC). Traditional access control mechanisms often prove inadequate due to the lack of efficient policy management. Their authorization time for queries is linear with respect to the number of access control rules in the policy, which greatly impedes access granularity, efficiency, and scale. To address this issue, we propose FECAC, a fine-grained and efficient capability-based access control mechanism for DON and DOC access queries. Specifically, FECAC builds a policy matching tree structure by translating the rule into matching properties in the tree node, which avoids authorizing queries by traversing the entire rule collection. We then introduce an authorization scheme to match the elements of access requests in a top-down manner, and check the rules with the internal properties of the data queries sublinearly, which efficiently combines the requests of DOC and DON. Further, we give a concrete operation of FECAC from query authorization scheme to execute data querying based on capabilities. Finally, we demonstrate the improved and more stable evaluation efficiency of FECAC compared to existing schemes.

Secure task matching has been a crucial research problem in crowdsourcing, requiring the alignment of workers’ preferences and requesters’ task requirements while ensuring user privacy and matching integrity. Recently, some researchers applied blockchain to crowdsourcing, either replacing the platform for decentralization or recording proofs for public verification to defend against malicious platforms. However, they still suffer from unitary coarse-grained matching models or expensive on-chain costs. To address these limitations, we propose PGVMatch, a privacy-aware and fine-grained crowdsourcing task-matching scheme with lightweight on-chain public verifiability. Our scheme is constructed on our newly proposed cryptographic primitive–Multi-authority Attribute-Based Keyword Search with Public Verifiability (MABKS-PV), which avoids access policy leakage and key escrow risks on a single authority, meanwhile adding constant-size proof generation and lightweight verification algorithms to a basic ABKS construction. In PGVMatch, requesters can select workers with fine-grained attribute demands, and workers can pick interested tasks with multi-keyword search, preserving dual-side privacy. The matching process is conducted off-chain, while constant-size proofs are recorded on-chain for efficient and public verification of matching integrity. Security analysis and extensive experiments on the Hyperledger Fabric blockchain demonstrate both the security and our superior performance. PGVMatch outperforms the existing scheme with the fastest matching result verification, achieving a 29% improvement in throughput and a 33% reduction in latency.

With the widespread adoption of electric vehicles (EVs), Vehicle-to-Vehicle (V2V) charging technology offers a more flexible solution for EV charging, significantly alleviating charging inconvenience, particularly in remote areas. However, the current V2V energy trading landscape lacks reliable management platforms with transparent transaction protocols. Furthermore, notable deficiencies exist in data privacy and security, which hinder the broader implementation of V2V charging services. Addressing these challenges, particularly the provision of secure and publicly auditable V2V charging without relying on centralized platforms or disclosing user privacy, has become a critical concern. For this reason, this paper presents PET, a decentralized Privacy-preserving Energy Trading system with public verifiability for EVs. PET is built upon the emerging blockchain technology to decentralize energy trading while ensuring accountability. We model energy demand-supply matching as range matching of locations and charge amounts between a buyer and sellers, and employ reverse auction to select the winner. For efficient and privacy-aware range proofs, we propose a novel batched hash chain-based range proof (BHW) primitive. In addition, PET integrates zk-SNARKs to verify payment correctness while maintaining user privacy. Our system supports public verifiability, entitling any third party to independently verify the transaction integrity. We analyze the privacy guarantees and public verifiability of PET. Extensive experiments implemented on Hyperledger Fabric further validate that PET delivers robust performance with a 4 × reduction in verification cost compared to that without batch proofs.

In crowdsensing, truth discovery (TD) has been extensively applied to resolve the data conflicts among publicly recruited workers and provide more reliable task data truths for task requesters. Data privacy and computation integrity are two major security concerns in TD due to the inherent untrustwor-thiness of the crowdsensing platform. Previous resolutions mostly focus on the design of privacy-preserving TD schemes while those leveraging blockchain to ensure TD integrity unfortunately sacrifice privacy or endure expensive on-chain overhead. In the presence of any misbehaviors, making TD integrity publicly and efficiently verifiable without compromising data privacy is imperative. This paper proposes p2STD, a Prlvacy-preservlng and Publicly verifiable scheme for generic Streaming TD (STD) in blockchain-enhanced crowdsensing. Unlike traditional TD in centralized crowdsensing, P2STD works in a decentralized architecture empowered by blockchain and fog, in which STD is jointly performed by fog nodes while the proof information is anchored to the blockchain for public verification. We take streaming confident-aware TD (S-CATD), the latest STD, as an instance and design secure aggregation protocols based on verifiable additive homomorphic secret sharing, concealing both task data and truth and generating publicly verifiable information. p2 STD is not specific to S-CATD and can be adapted to other iterative STD algorithms. Security analysis proves the desired privacy and public verifiability achieved in p2 STD. Experimental evaluations demonstrate that P2STD has high accuracy, lower computational and communication overhead, offering public verifiability with additional subtle gas cost than others.

Vehicular Edge Computing (VEC) integrates mobile edge computing with traditional vehicular networks, which shifts the majority of computation and storage workload of resource-constrained vehicles to the edge nodes. The high mobility of vehicles usually leads to frequent network changes and connection interruptions, making data sharing more challenging in such dynamic and unstable environments. To address this issue, cache-based content delivery is considered a promising solution for efficient data sharing in VEC. However, access control and fair incentive distribution in privacy-aware data sharing are rarely taken into account in prior VEC-oriented studies. In this paper, we propose RFIP-VEC, a Revocable access control scheme with Fair Incentive for Privacy-aware content delivery in VEC. Specifically, to enable anonymous authentication and conditional revocation, we construct a secure group signature scheme with formally proved security guarantees. Subsequently, based on our group signature scheme, we design a two-layer access control framework by employing proxy re-encryption. We also establish an evolutionary game theory model to analyze the effectiveness and fairness of the fair incentive in our scheme. Thus, our scheme can achieve flexible access control and fair incentive distribution with the assistance of edge nodes. Security analysis and experimental results demonstrate that the proposed scheme can achieve security goals with affordable cost in terms of network performance in VEC.

The maturing blockchain technology has gradually promoted decentralized data storage from cryptocurrencies to other applications, such as trust management, resulting in new challenges based on specific scenarios. Taking the mobile trust blockchain within a vehicular network as an example, many users require the system to process massive traffic information for accurate trust assessment, preserve data reliably, and respond quickly. While existing vehicular blockchain systems ensure immutability, transparency, and traceability, they are limited in terms of scalability, performance, and security. To address these issues, this paper proposes a novel decentralized vehicle trust management solution and a well-matched blockchain framework that provides both security and performance. The paper primarily addresses two issues: i) To provide accurate trust evaluation, the trust model adopts a decentralized and peer-review-based trust computation method secured by trusted execution environments (TEEs). ii) To ensure reliable trust management, a multi-shard blockchain framework is developed with a novel hierarchical Byzantine consensus protocol, improving efficiency and security while providing high scalability and performance. The proposed scheme combines the decentralized trust model with a multi-shard blockchain, preserving trust information through a hierarchical consensus protocol. Finally, real-world experiments are conducted by developing a testbed deployed on both local and cloud servers for performance measurements.

Privacy-aware task allocation/matching has been an active research focus in crowdsourcing. However, existing studies focus on an honest-but-curious assumption and a single-attribute matching model. There is a lack of adequate attention paid to scheme designs against malicious behaviors and supporting user-side personalized task matching over multiple attributes. A few recent works employ blockchain and cryptographic techniques to decentralize the matching procedure with verifiable and privacy-preserving on-chain executions. However, they still bear expensive on-chain overhead. In this paper, we propose VP$^{2}$-Match, a blockchain-assisted (publicly) verifiable privacy-aware crowdsourcing task matching scheme with personalization. VP$^{2}$-Match extends symmetric hidden vector encryption for user-side expressive matching without compromising their privacy. It avoids costly on-chain matching by letting the blockchain only store evidence/proofs for public verifiability of the matching correctness and for enforcing fair interactions against misbehaviors. Specifically, we construct extended attribute sets and solve matching verification by an algorithmic reduction into subset verification with an accumulator for proof generation. Formal security proof and extensive comparison experiments on Ethereum demonstrate the provable security and better performance of VP$^{2}$-Match, respectively.

Crowdsourcing has gained many developments and wide applications in our daily life. Traditional centralized crowdsourcing systems suffer from high management costs and low efficiency. The recent advance in the blockchain technology has enabled the construction of decentralized crowdsourcing systems, which can overcome the limitations of centralized systems and make crowdsourcing solution reuse possible. However, such systems also bring new security and privacy challenges. For instance, transactions on blockchain are publicly visible which can lead to privacy leakage of crowdsourcing users. Moreover, unfair exchange is a critical issue on these platforms. In this paper, we propose a privacy-preserving and fair crowdsourcing framework with fine-grained reuse based on blockchain to meet the security requirements for decentralized crowdsourcing. Specifically, we construct one-address-only (OAO) authentication to ensure the uniqueness of the participant’s address in the crowdsourcing process. Additionally, We design a submit-then-open method with commitments to resist the “free-riding" and “false-reporting" attacks. Thus, fair exchange between entities can be guaranteed. To ensure data confidentiality and fine-grained solution item reuse, we employ pairing-based cryptography to generate an encryption key and ensure flexible authorization reuse. We also adopt stealth authorization techniques to ensure privacy-preserving access authorization during the reuse phase. Finally, security analysis and implementation results have shown that the proposed framework can effectively achieve privacy-preserving and fair crowdsourcing as well as fine-grained crowdsourcing reuse. Specifically, the gas consumption in the reuse phase is reduced by approximately 49% to 81% compared to the normal operation, which significantly improves the efficiency of blockchain applications.

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents an incentive mechanism HERALD*, that takes into account the uncertainty and hidden bids of tasks without real-time constraints. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.

Truth discovery is an effective and compelling approach to addressing data conflicts among different workers and offers more trustworthy truths to task requesters in crowdsourcing. Prior research either focused on studying more accurate truth discovery algorithms or aimed to protect data privacy from the centralized and honest-but-curious crowdsourcing platforms. They all overlooked the stronger threats from the malicious crowdsourcing platform (e.g., may return incorrectly estimated truths) and many critical issues inherited from centralization. This paper proposes a blockchain-based decentralized truth discovery scheme for crowdsourcing, with computation integrity guarantees against malicious participants and support for efficient processing of generic streaming data. We adopt the idea of hybrid storage and computations to ease the expensive on-chain cost. Workers are grouped for off-chain partial truth estimation and smart contracts are leveraged for on-chain final truth aggregation. To prevent any improper computations from malicious entities, we record the hashes of data and worker weights on-chain occasionally. Through theoretical analysis and extensive experiments over real-world and synthetic datasets implemented in Ethereum, we demonstrate that our scheme 1) achieves our reliability goals with certain privacy assurance; 2) exhibits a higher truth estimation accuracy than existing approaches and a lower gas consumption than the baseline.

As a new type of task execution mode, crowdsourcing makes use of crowd/worker intelligence to collaboratively complete diverse tasks published by task requesters. Quality assessment is an important stage in crowdsourcing as the publicly recruited workers often vary in reliability when performing tasks. Prior works on crowdsourcing quality assessment either ignore the possible privacy disclosure from the task data or are vulnerable to biased evaluation from malicious evaluators. In this paper, we propose a privacy-aware verifiable crowdsourcing quality assessment scheme UNITE against semi-honest and malicious adversaries. UNITE explores federated learning for privacy-aware training of task models, which serves as an indicator of quality assessment. To prevent attackers from deducing the task data from model gradients, we design a secure model update protocol based on differential privacy and perform it with blockchain smart contracts for trustworthy model aggregation. In the presence of malicious requesters providing incorrect assessments, we exploit Pedersen Commitment to generate evidence, which is recorded on-chain with some metadata for public audit. Detailed privacy analysis demonstrates that our differential privacy scheme satisfies (ε,δ)-local differential privacy. Finally, we conducted extensive experiments on two real-world datasets and deployed the smart contracts on Hyperledger Fabric to demonstrate good accuracy and both on-chain and off-chain performance.

Privacy-preserving authentication is designed to protect vehicular ad-hoc networks (VANETs) from illegitimate users and fake messages while maintaining the privacy of legitimate users’ identities. However, existing authentication schemes have disadvantages such as non-transparent certificate issuance and revocation, high identity authentication and certificate revocation overhead. In this paper, we propose an efficient privacy-preserving authentication scheme with on-chain certificate management (PACM) in VANETs, where the service manager (SM) of each domain serves as a node of the blockchain to build a distributed system. Specifically, based on elliptic curve cryptography (ECC) and exclusive-OR operations, we achieve secure and lightweight mutual authentication between vehicles and roadside units (RSUs) by regularly updated pseudonyms. Then, we adopt the blockchain to record the issuance and revocation of all certificates, which makes SM’s activities transparent. Moreover, we introduce the counting garbled bloom filter (CGBF) to enable fast query and revocation of certificates. Besides, we design a non-forgeable and non-repudiable billing mechanism based on the hash chain technology. Security analysis and experimental results show that PACM achieves stronger security with less overhead.

The ubiquity of crowdsourcing has reshaped the static sensor-enabled data sensing paradigm with cost efficiency and flexibility. Still, most existing triangular crowdsourcing systems only work under the centralized trust assumption and suffer from various attacks mounted by malicious users. Although incorporating the emerging blockchain technology into crowdsourcing provides a possibility to mitigate some of the issues, how to concretely implement the crucial components and their functionalities in a verifiable and privacy-aware manner remains unaddressed. In this article, we present BRPC, a blockchain-based decentralized system for general crowdsourcing. BRPC integrates the confident-aware truth discovery algorithm to provide task requesters with reliable task truths while evaluating each worker’s data quality. To mitigate the biased evaluation of malicious requesters, we propose a privacy-aware verification protocol leveraging the threshold Paillier cryptosystem, with which a certain number of workers can collaboratively verify the evaluation results without knowing any sensory data. Furthermore, we define the three roles of a user and elaborate a comprehensive reputation evaluation model enforced by smart contracts for its trustworthy running. Financial and social incentives are both offered to motivate users’ honest participation. Finally, we implement a prototype of BRPC and deploy it on the Ethereum blockchain. Theoretical analyses and experiment results show its security and practicality.

Vehicular Edge Computing (VEC) is an integration of Mobile Edge Computing with traditional vehicular networks, which aims to shift computing, communication, and storage resources to the edge of networks and is more close to vehicles. Due to the high mobility of vehicles, connection interruption and network changes may frequently occur. To tackle this issue, cache-based content delivery is regarded as a promising solution to achieve efficient data sharing in VEC. However, privacy-preserving access control and fair incentive distribution are rarely taken into account in prior VEC-oriented studies. In this paper, we propose an efficient and secure access control scheme for providing cache-based content delivery in VEC. Specifically, we construct two layer access control to enable flexible access control and fair incentive distribution with the assistance of edge nodes. Moreover, we construct a group signature-based scheme to achieve anonymous authentication and conditional revocation. The performance analysis shows that our secure scheme has an acceptable effect on network performance.

Vehicular crowdsourcing networks (VCNs) enable vehicles to provide or obtain traffic-related services in a costefficient and flexible manner. Therefore, it is crucial to provide trusted management in VCNs for high reliability towards both service producers and consumers. However, most recent VCN platforms rely on a third party to manage crowdsourcing services which might be not fully trusted by users. For the issue, this paper proposes a blockchain-based trust management scheme for VCNs to provide a decentralized and trusted service management. A comprehensive trust evaluation model (TEM) is designed to quantify the trust degree of each vehicular node, and a vehicle-trust blockchain framework called VTchain is proposed to preserve the trust values of nodes while guaranteeing transparency and trustworthiness. Particularly, we leverage a trusted execution environment (TEE) to provide secure trust evaluation to tackle possible untrusted road-side units. In addition, we introduce TEM-based Proof of Trust to support blockchain maintenance, which works together with an efficient consensus algorithm Zyzzyva for improved scalability. Finally, extensive experiments are conducted by developing a testbed deployed on cloud servers for measurements.

The large-scale deployment of eHealth systems has brought deep impact on human society. However, the centralized Electronic health records (EHRs) outsourcing system faces some critical security and privacy issues, which have raised wide concerns in both academia and industry. Moreover, the patients lose control of their health data. There is a need to construct a decentralized and secure EHRs with more flexible control by patients themselves instead of the third party. Fortunately, we observe that the characteristics of blockchain technology such as decentralization, immutability, and auditability perfectly match these aforementioned requirements. Specifically, to satisfy our application requirements, we build a consortium blockchain (PESchain) which is maintained by a set of medical institutions. The EHRs of patients are encrypted and stored in the medical institutions by local cloud while the corresponding hash values are stored on PESchain. Moreover, to enable privacy-preserving EHRs sharing, we construct a stealth authorization scheme to achieve access authorization delivery on the blockchain. Besides, we pack the transactions according to different types to guarantee efficient block deletion. The security analysis and performance evaluation show that PESchain is secure and practical for EHRs sharing.

Pervasive mobile devices and their advances in sensing and networking have led to an emerging mobile sensing paradigm. The diversity of mobile users and the openness of sensing systems raise several crucial concerns for users' privacy, data quantity, and quality. Although different aspects of these issues were addressed separately in existing researches, there is still a need to provide a holistic solution for secure and privacy-aware mobile sensing. In this paper, we propose a privacy-aware and trustworthy mobile sensing scheme with fair incentives. Leveraging group signature, (partial) blind signature, and limited number of pseudonyms technologies, our scheme enables well-behaved users to contribute their data anonymously, and prevents both greedy and malicious users from abusing the privacy protection. Moreover, we design a fair incentive scheme to stimulate users to contribute high-quality data, based on the data quality and the reputation feedback level. Security analysis demonstrates that our proposed scheme achieves the security goals. Extensive evaluation results are presented which demonstrate the effectiveness and efficiency of our scheme.

Ubiquitous mobile devices with rich sensors and advanced communication capabilities have given rise to mobile crowdsensing systems. The diverse reliabilities of mobile users and the openness of sensing paradigms raise concerns for data trustworthiness, user privacy, and incentive provision. Instead of considering these issues as isolated modules in most existing researches, we comprehensively capture both conflict and inner-relationship among them. In this paper, we propose a holistic solution for trustworthy and privacy-aware mobile crowdsensing with no need of a trusted third party. Specifically, leveraging cryptographic technologies, we devise a series of protocols to enable benign users to request tasks, contribute their data, and earn rewards anonymously without any data linkability. Meanwhile, an anonymous trust/reputation model is seamlessly integrated into our scheme, which acts as reference for our fair incentive design, and provides evidence to detect malicious users who degrade the data trustworthiness. Particularly, we first propose the idea of limiting the number of issued pseudonyms which serves to efficiently tackle the anonymity abuse issue. Security analysis demonstrates that our proposed scheme achieves stronger security with resilience against possible collusion attacks. Extensive simulations are presented which demonstrate the efficiency and practicality of our scheme.

This paper considers a practical crowd-outsourcing system model for location-based services which has become increasingly popular due to the rapid proliferation of location-aware mobile devices. In our system, multiple data owners (DOs) outsource their small number of points of interests (POIs) to the location-based service provider (LBSP), then LBSP manages these POIs datasets and allows users to share information and perform top-k queries according to their own preferences. One crucial problem in this system is how to deal with the untrusted LBSP, who may return fake or incorrect query results to users for certain motives. However, the traditional top-k query and verification schemes, where only an individual DO and a single query attribute are considered, cannot be efficiently applied to our system, as users have distinct query preferences and the query may involve multiple DOs. In this paper, we design a dominant authentication graph DAUG) to process the multi-attribute data on multiple datasets efficiently, and two schemes are proposed for users to verify the integrity of the query result based on DAUG. Finally, theoretical analysis and simulation results show our superiority to the previous scheme in terms of effectiveness and efficiency.