Along with applying blockchain technology in the health care service system (HSS), many distributed solutions have been proposed to solve the centralized, data island problems. Although blockchain-enabled HSS has significantly changed the traditional management mode of medical Big Data [e.g., electronic medical records (EMRs)], some new problems such as ledger corpulence, data searchable security, and quantum attack threat are exposed. In this article, we first introduce an efficient EMRs management model called on-chain ledger and off-chain storage (OLOS), in which the indexes of EMRs are uploaded on public blockchain and the real EMRs data are stored in native server. This model can relieve the public ledger and save the public cloud space. Meanwhile, it can protect data security by keeping off the direct operation on real EMRs data. Then, to protect the cross-institutional EMRs sharing security and improve the quantum-resist ability, we proposed a secure keyword-searchable attribute-based encryption (KS-ABE) scheme based on lattice cryptography. Moreover, the scheme can prove to be secure against adaptive chosen-keyword attack and adaptive chosen-policy attack in the random oracle model. Besides, comparison analysis and experimental results show that our KS-ABE scheme has fewer communication costs and smaller key sizes than similar literary works.

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.

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.

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 paper, 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.