With the rapid development of the Internet of Vehicles (IoV), achieving trustworthy vehicle position verification while preserving location privacy has become a key requirement in intelligent traffic supervision scenarios such as defense control zones and urban restricted-access areas. Existing privacy-preserving schemes have difficulty simultaneously supporting accurate determination of complex-shaped prohibited areas and efficient computation, and still face malicious attacks such as interference with verification procedures, tampering with communication processes, and privacy inference when determining the positional relationship between vehicles and prohibited areas. To address these issues, this paper proposes an efficient privacy-preserving position verification (PPPV) scheme based on secure multi-party computation (MPC). The scheme supports arbitrary polygonal prohibited areas, including convex, concave, and self-intersecting polygons, thereby improving its applicability in complex IoV supervision scenarios. Based on an improved cross-product determination method, this paper constructs an efficient PPPV protocol under the semi-honest model, achieving near-plaintext computational efficiency while protecting the privacy of both vehicle locations and area boundaries. To resist malicious attacks, this paper further combines Paillier homomorphic encryption, the cut-and-choose method, and zero-knowledge proof to construct a secure PPPV protocol under the malicious model, which can effectively prevent protocol deviations, result tampering, and inference attacks. This paper also conducts formal security proof based on the real/ideal model paradigm, and evaluates the performance of the scheme through benchmark experiments and attack experiments. Experimental results show that the scheme achieves a good balance among efficiency, applicability, and security, providing a deployable trustworthy position verification mechanism for next-generation IoV intelligent supervision applications.

Introduced by Google in 2016, federated learning (FL) is a distributed machine learning framework to ensure data privacy amid the surge in big data. FL enables secure data sharing without accessing local data. Despite its advantages, it faces challenges due to the limited participation of the data owner. To address this, this article proposes the model value transfer incentive (MVTI) to enhance FL incentives for Artificial Intelligence of Things (AIoT). MVTI allows active participation of data requesters in FL training, addressing limited data owner engagement, and facilitating personalized model construction. The integrated model bail and contribution assessment mechanism ensures fair benefit redistribution. Using smart contracts (SCs) and interplanetary file system (IPFS) enhances security and reliability, ensuring transparent and tamper-resistant execution for secure transactions and data integrity. Our experiments highlight MVTI’s superiority in addressing FL incentive challenges for AIoT compared to state-of-the-art baselines on real-world datasets. We also demonstrate the compatibility of multiple gradient protections with incentive mechanisms, especially with gradient compression. The proposed SC-MVTI scheme is resilient and demonstrates the potential to significantly improve the overall efficacy of the FL system within incentive frameworks.

Internet of Medical Things (IoMT) has garnered significant research attention from both academic and medical institutions. However, the sensitive medical data involved in IoMT raises security and privacy concerns. To mitigate these, ring signature has surfaced as a proper solution, which offers unforgeability and anonymity. Unfortunately, most multi-signer ring signature schemes require a predetermined number of signers and are difficult to adjust dynamically. Additionally, traditional ring signatures have limited adaptability for IoMT due to their reliance on a single entity. It is challenging to effectively define different signature permissions for users of various entities, such as patients and doctors. Therefore, research focusing on constructing a dynamic multi-signer ring signature for multi-party participation remains a critical and ongoing challenge. In this paper, we present ASMR, an anonymity-enhanced sequential multi-signer ring signature scheme for secure medical data sharing in IoMT. ASMR contains two different rings, PR and DR, for patients and doctors, respectively. It allows patients in PR to anonymously sign their electronic healthcare record (EHR) owned by doctors in DR, overcoming the reliance on a single entity in existing approaches while enhancing the anonymity of the signature. Meanwhile, ASMR introduces the concept of signature chaining, allowing multiple users in DR to co-sign information in sequence. In addition, it ensures that each generated signature is traceable, offering a transparent system. We also formally prove the security of ASMR in the random oracle model. Comprehensive performance evaluations indicate that ASMR excels in both computational and storage overhead. In the best case, computational overhead is reduced by approximately 4.7×-61.7×, while storage overhead is reduced by approximately 26.7×-212.8× compared to prior arts.

The Industrial Internet of Things (IIoT) has been widely studied, which dramatically enhanced the manufacturing efficiency and service elasticity. However, how to ensure the data confidentiality and security in the event-driven publish/subscribe communication model becomes a cumbersome problem. To address this concern, ring signatures have been researched deeply. Nevertheless, existing solutions have large computational burdens and neglect to incorporate reporting and tracing features, which makes it impractical for IIoT. In this way, research focus on designing an efficient report and traceable ring signature is still far-reaching. In this article, we propose RAT ring, a novel report and traceable ring signature, which provides publisher authentication, anonymous communication, reporting, and tracing. To achieve this, we adopt the zero knowledge proof to verify the authenticity of publisher data, and the signature of knowledge to trace the signature. Then, we formalize and prove the security of our scheme. Eventually, through comprehensive performance evaluation, our scheme outperforms prior works by approximately up to 51 times in terms of total computational overhead. These results demonstrate that our design is practical and effective for data privacy-preserving in IIoT.

Data fusion in the Internet of Things (IoT) is based on the intersection and union problems of sets without complete set restrictions to achieve accurate and reliable location services. However, protecting users’ privacy in IoT is challenging, for which a solution is proposed herein in conjunction with secure multi-party computation (MPC). Using the proposed solutions, users’ privacy can be ensured while achieving accurate and reliable location positioning. Current privacy-preserving schemes used for data fusion require a set’s elements belonging to an appropriate complete set, and the range of elements used by such schemes is usually integers. Moreover, a unified range of all elements in a set cannot be determined in real-time for practical scenarios. Limiting the range of elements to integers is also impossible, because the type of data collected by IoT devices is uncertain. To address these issues, new encoding and transformation methods are proposed herein to map a set of rational numbers to the Cartesian coordinate system. The NTRU encryption scheme is used to design the intersection and union MPC protocols of rational numbers’ sets without any complete set restriction under the semi-honest model. Then, to prevent possible malicious behaviors in the semi-honest model protocols, MPC protocols for the intersection and union of rational numbers’ sets are designed without any complete set. The security of the protocols is demonstrated by applying the real/ideal model paradigm. The comparison of the proposed protocols with other schemes shows that our protocols have higher computational efficiency.

Internet of Medical Things (IoMT) aggregates a series of smart medical devices and fully uses the collected health data to improve user experience, medical resource utilization, and full life cycle protection. However, privacy leakage, data loss, and inefficient sharing problems are still serious in the data-sharing process between different smart medical devices. This article first introduces an efficient privacy-preserving model with blockchain to construct a secure data-sharing mechanism between different device nodes. This model utilizes distributed storage form to solve the centralized management problem and provides a fundamental secret reconstruction and retrieval framework. Then, a lightweight$(t,n)$-threshold secret sharing$(t/n$-SS) scheme is designed to strengthen the medical data-sharing security and efficiency. It utilizes the interleaving encode technology to decrease the length of original message into$n$small shares. These small shares are also suitable for data transmission and processing with a more energy-efficient way. It can protect privacy by destroying the data’s semantic meaning. Meanwhile, it only needs less than$t (t\leq n)$shares to recover the original secrets, making the sharing process more efficient. Moreover, the performance evaluations of transaction processing in IoMT show that the proposed model is very stable. The simulation and performance evaluation results show that this$t/n$-SS scheme is energy efficient, storage saving, and strong fault tolerance than similar literature.

Electronic medical records (EMRs) are the most critical data in human health management. As in traditional centralized healthcare service systems (HSSs), user privacy security, EMRs data leakage, tampering, and island are some significant problems. However, blockchain is a promising technology to protect the privacy and realize cross-institutional data sharing for solving these problems. In this article, a novel peer-to-peer EMRs data management and trading system called healthchain has been proposed based on consortium blockchain technology. Through this distributed system, the patient can access their EMRs in different institutions freely, and the EMRs can be traded among different users conveniently. Then, to balance EMRs data supply and demand, we establish a Stackelberg pricing model to evaluate EMRs data providers and consumers' interactions. The optimal unit price and data amounts can be found by applying the backward induction method, and the maximizing benefits of the participants can be obtained by achieving the Nash equilibrium in the proposed game. Moreover, security analysis shows the healthchain can provide secure EMRs management and trading, and the simulation results show that the proposed pricing model can help the healthchain achieve social welfare maximization.