Blockchain-enabled Internet of Medical Things (BIoMT) has received extensive attention and in-depth research to solve the centralized, data island problems with the rapid developments of blockchain-related technologies. However, many different chains with different data structures, consensus protocols, and cryptographic algorithms are constructed, which brings a new “data island” problem. Meanwhile, the cryptographic algorithms used in most current BIoMT systems are weak against quantum attacks. In this article, a cross-chain privacy-preserving (CCPP) model and a designated verifier proxy signature (DVPS) scheme have been proposed. This CCPP model is equipped with the relay chain technology and DVPS to achieve secure cross-chain medical data-sharing among different BIoMT systems. The DVPS scheme is constructed with lattice theory, which can achieve signer proxy, designated user verification, and anti-quantum attack. Then, the security proof shows that the proposed DVPS can capture the security properties of correctness, unforgeability, the signer’s anonymity, and nontransferability. The performance evaluations show that the cross-chain transactions are efficient and stable with the transaction number increasing, and the proposed DVPS is efficient about the key size, time consumption, and energy consumption. This work can also improve the privacy security of system users and medical data in BIoMT systems and promote the value play of medical data.

Blockchain technology brings a method for cross-institution health data sharing through the systems of the Internet of Medical Things (IoMT). As different medical institutions compete to establish their own blockchain ledgers, it leads to new problems of “data island”. In this paper, a relay chain-based multi-chain fusion (MCF) model has been designed for blockchain-enabled IoMT (BIoMT), which can achieve cross-institution health data sharing by composing different blockchains together. In this MCF model, the existing patient private health chain, medical institution chain, and government supervision chain compose a cross-chain health data-sharing platform, which extends the storage capacity of health data, and the capacity of data sharing among different departments, institutions, and fields. Meanwhile, a cross-chain transaction model has been established which helps to achieve secure cross-chain transactions among different medical institutions. Then, to guarantee user privacy in the cross-chain transaction process, a designated verifier signature (DVS) scheme is proposed. Only the designated verifier can verify this DVS and other users cannot identify the real signer. This DVS also can achieve the anonymity of the signer as the third party cannot distinguish the signature generated by the signer or the verifier. Moreover, the proposed DVS scheme can be proved to capture the unforgeability, non-transferability, and signer anonymity with the random oracle model. The theoretical analyses and efficiency comparisons are given which show the efficiency of the proposed DVS scheme compared with similar schemes. The performance simulation of the cross-chain transaction shows that the MCF model is secure and practical for cross-chain health data sharing among different BIoMT systems.

Private set intersection cardinality (PSI-CA) allows two parties to learn the size of the intersection between two private sets without revealing other additional information, which is a promising technique to solve privacy concerns in contact tracing. Efficient PSI protocols typically use oblivious transfer, involving multiple rounds of interaction and leading to heavy local computation overheads and protocol delays, especially when interacting with many receivers. Cloud-assisted PSI-CA is a better solution as it relieves participants' burdens of computation and communication. However, cloud servers may return incorrect or incomplete results for some reason, leading to an incorrectness issue. At present, to our knowledge, existing cloud-assisted PSI-CA protocols cannot address such a concern. To address this, we propose two specific verifiable cloud-assisted PSI-CA protocols: one based on a two-server protocol and the other on a single-server protocol. Further, we employ Cuckoo hashing to optimize these two protocols, enabling the receiver's computational costs independent of the size of the sender's set. We also prove the security of the protocols and implement them. Finally, we analyze and discuss their performance demonstrating that the single-server verifiable PSI-CA protocol does not introduce significant computation or communication costs while adding functionalities.

Laconic Private Set Intersection (LPSI) is a type of PSI protocols characterized by the requirement of only two-round interactions and by having a reused message in the first round that is independent of the set size. Recently, Aranha et al. (CCS'2022) proposed a LPSI protocol that utilizes the pairing-based accumulator. However, this protocol heavily relies on time-consuming bilinear pairing operations, which can potentially cause a bottleneck. Furthermore, in certain scenarios like contact tracing, it is sufficient to only reveal the intersection cardinality. To tackle this problem and expand on its functionalities, we introduce a cloud-assisted two-party LPSI cardinality (TLPSI-CA) that inherits the properties of LPSI. Interestingly, the cloud-assisted TLPSI-CA eliminates the direct interaction between the sender and receiver, enabling the sender's message to be reused across any number of protocol executions. Besides, we further extend it to the multi-party scenario, which also possesses laconic properties. Then, we prove the two protocols' security in achieving the defined ideal functionalities. Finally, we evaluate the performance of both protocols and find that TLPSI-CA successfully reduces the local computation costs for participants. Additionally, the multi-party protocol performs similarly to TLPSI-CA, with the exception of the higher communication costs incurred by the receiver.

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.