Blockchain technology has gained significant prominence in recent years due to its public, distributed and decentration characteristics, which was widely applied in all walks of life requiring distributed trustless consensus. However, most cryptographic protocols used in current blockchain networks are susceptible to the quantum attack with rapid development of a sufficiently large quantum computer. In this paper, we first give an overview of the vulnerabilities of modern blockchain networks to a quantum adversary and some potential post-quantum mitigation methods. Then, a new lattice-based signature scheme has been proposed, which can be used to secure the blockchain network over existing classical channels. Meanwhile, the public and private keys are generated by the Bonsai Trees technology with RandBasis algorithm from the root keys, which can not only ensure the randomness, but construct a lightweight nondeterministic wallets. Then, the proposed scheme can be proved secure in random oracle model, and it is also more efficient than the similar literatures. In addition, we also give the detail description of the post-quantum blockchain transaction. Furthermore, this work can help to rich the research on the future post-quantum blockchain (PQB).

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 paper 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≤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.

Polyadenylation Poly(A) is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and mRNA metabolism. In this work, we propose a deep dual-dynamic context-aware Poly(A) signal prediction model, called multiscale convolution with self-attention networks (MCANet), to adaptively uncover the spatial–temporal contextual dependence information. Specifically, the model automatically learns and strengthens informative features from the temporalwise and the spatialwise dimension. The identity connectivity performs contextual feature maps of Poly(A) data by direct connections from previous layers to subsequent layers. Then, a fully parametric rectified linear unit (FP-RELU) with dual-dynamic coefficients is devised to make the training of the model easier and enhance the generalization ability. A cross-entropy loss (CL) function is designed to make the model focus on samples that are easy to misclassify. Experiments on different Poly(A) signals demonstrate the superior performance of the proposed MCANet, and an ablation study shows the effectiveness of the network design for the feature learning and prediction of Poly(A) signals.

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.

The Internet of Things (IoT) plays an essential role in people’s daily lives, such as healthcare, home, traffic, industry, and so on. With the increase in IoT devices, there emerge many security issues of data loss, privacy leakage, and information temper in IoT network applications. Even with the development of quantum computing, most current information systems are weak to quantum attacks with traditional cryptographic algorithms. This paper first establishes a general security model for these IoT network applications, which comprises the blockchain and a post-quantum secure identity-based signature (PQ-IDS) scheme. This model divides these IoT networks into three layers: perceptual, network, and application, which can protect data security and user privacy in the whole data-sharing process. The proposed PQ-IDS scheme is based on lattice cryptography. Bimodal Gaussian distribution and the discrete Gaussian sample algorithm are applied to construct the fundamental difficulty problem of lattice assumption. This assumption can help resist the quantum attack for information exchange among IoT devices. Meanwhile, the signature mechanism with IoT devices’ identity can guarantee non-repudiation of information signatures. Then, the security proof shows that the proposed PQ-IDS can obtain the security properties of unforgeability, non-repudiation, and non-transferability. The efficiency comparisons and performance evaluations show that the proposed PQ-IDS has good efficiency and practice in IoT network applications.

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 article, a relay chain-based multichain 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, nontransferability, 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.