An instructional management system (IMS) regularly manages student data with a centralized database, which may be prone to single points of failure and data tampering. Blockchain can effectively solve the above problems due to its decentralization and immutability. However, the immutability of blockchain restricts the edit of data in some cases (e.g., incorrect score records). In this paper, we propose an advanced decentralized instructional management framework based on redactable blockchain, wherein chain nodes possess the capability to autonomously revise their initiated transactions with regular chameleon hash functions. Concurrently, system users can anonymously report discrepancies or erroneous entries within the blockchain. When such reports are validated and malicious activities are confirmed, the review team can undertake decentralized modifications to rectify the malicious content with our proposed scheme. In addition, we conduct a security analysis of the scheme, evaluating its correctness, collision resistance, and claimability. Finally, experimental results demonstrate that the scheme is practical.

Session-based multimedia recommendation in edge computing remains an important issue for boosting the utilization of services since service composition has increasingly attracted attention. Existing session-based recommendations (SBRs) model the session sequence with multilevel feature extraction in graph neural networks (GNNs). However, multilevel feature extraction in disentangled graph neural networks causes over-smoothing and privacy leakage. To address the aforementioned problems, Secure and Efficient Session-based Multimedia Recommendation (SeSMR) model is proposed. In the proposed SeSMR model, based on BGV homomorphic encryption, a ciphertext training submodel is proposed to address the privacy leakage, ensuring the security in SBR. Furthermore, based on the reinforcement of feature activation, a residual attention mechanism is proposed to mitigate over-smoothing while maintaining the independence of multiple features. Finally, based on location coding, a soft attention mechanism is proposed to improve the recommendation accuracy, by introducing the position difference information between items into intra-session and inter-session scenarios. Experiments demonstrate that both Recall and MRR metrics exhibit nearly 2% to 5% improvement.

Summary Service evaluation model is an essential ingredient in service‐oriented Internet of things (IoT) architecture. Generally, traditional models allow each user to submit their comments with respect to IoT services individually. However, these kind of models are fragile to resist various attacks, like comment denial attacks, and Sybil attacks, which may decrease the comments submission rate. In this article, we propose a new aggregation digital signature scheme to resolve the problem of comments aggregation, which may aggregate different comments into one with high efficiency and security level. Based on the new aggregation digital signature scheme, we further put forward a new service evaluation model named Tesia allowing specific users to submit the comments as a group in IoT networks. More specifically, they aggregate comments and assign one user as a submitter to submit these comments. In addition, we introduce the synchronization token mechanism into the new service evaluation model, to assure that all users in the group may sign their comments one by one, and the last one who receives the token is assigned as the final submitter. Tesia has more acceptable robustness and can greatly improve the comments submission rate with rather lower submission delay time.

Aggregate signatures allow someone to aggregate multiple signatures into one signature, which is suitable for resource-constrained and computationally inefficient environments. Identify-based aggregate signature can solve the storage problem of public key certificates while achieving efficient signature verification. However, in most of the identity-based aggregate signature schemes, the user identity revocation process is time-consuming and cannot resist quantum attacks. To solve above problems, this paper proposes a lattice-based aggregate signature scheme with dynamic identity revocation by combining lattice-based cryptography and an aggregate signature scheme. The security of the proposed lattice-based aggregate signature scheme with dynamic identity revocation has been proved in the random oracle model. In addition, the verification efficiency of the aggregate signature has been improved compared with multiple different signatures. Much of the data transfer in Vehicular Ad Hoc Network (VANET) is carried out wirelessly, which makes VANET vulnerable to identity spoofing attacks. Identity authentication technology can prevent attackers from impersonating legitimate users, thus ensuring the security of VANET. Based on the proposed lattice-based aggregate signature scheme with dynamic identity revocation, this paper proposes a lattice-based batch authentication scheme with dynamic identity revocation in VANET. Through the proposed batch authentication scheme, we can effectively resist the impersonation attack of VANET in the quantum computer environment, and the efficiency of authentication is improved.

With the development of network technology, privacy protection and users anonymity become a new research hotspot. The existing blockchain privacy-aware public key infrastructure (PKI) model can ensure the privacy of users in the authentication process to a certain extent, but there are still problems of the storage and leakage of users' keys. This paper first proposes a strong forward-secure ring signature scheme based on RSA, which ensures the anonymity of the signing users and the forward-backward security of the keys. Then, by introducing the ring signature technology into the privacy-aware PKI model, this paper proposes a privacy-aware PKI model with strong forward security based on block chains, which not only ensures the users' identity privacy, but also solves the problem of the storage and leakage of the users' keys, greatly improving the success rate and security of the users' identity authentication. Finally, this paper applies the proposed PKI model to anonymous transactions, designs a privacy-aware anonymous transaction model with strong forward security, realizing anonymous transactions without relying on trusted third parties, and implementing users' privacy protection.

Signcryption technology combines signature and encryption operations in a single step to achieve message authentication and confidentiality. The ordinary signcryption technology cannot realize communication between two different cryptographic systems. Therefore, to implement efficient communication between different cryptosystems and resist quantum attacks, this paper proposes a lattice-based efficient heterogeneous signcryption scheme. The heterogeneous signcryption scheme is proved to be secure assuming the hardness of small integer solution and learning with errors problems. Then this paper applies the lattice-based efficient heterogeneous signcryption scheme to the federated learning system to achieve the transmission of confidential information, and designs an intelligent federated learning system on lattice-based efficient heterogeneous signcryption. This system realizes federated learning and the quantum security of data transmission while preserving private data.

Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity update and synchronization scheme is also proposed to ensure the reliability and security of communication. Meanwhile, our performance evaluations and simulations show that the proposed framework is more effective than several standard schemes with respect to the ability against various attacks, security, and overhead.

vMixing coins strategy can realize the anonymity of user information, thereby protecting the user's privacy. Ideally, the blacklist is public information and all bad coins are recorded in it. However, due to the failure of some bad coins to be registered in the blacklist in time, users can only obtain part of the blacklist information, which allows illegal criminals to take advantage of it. How to prevent illegal activities under the partial information blacklist and how to design coins' quality updating rule rationally have become open issues in mixing coins. The updating rule of coins' quality in mixing is addressed since illegal criminals may carry out illegal activities, for example, money laundering. ImpSuic, an improved suicide strategy, is proposed as a new quality updating rule. The intuition is: all coins of the one who has the highest bad coins according to the blacklist, are recorded as bad coins. On the other hand, the coins' quality of others remain unchanged. Besides, linear programming is introduced into ImpSuic strategy to predict the maximum utility after mixing coins, which facilitates users to make reasonable decisions before mixing coins. Simulation results show that the quality updating rule in ImpSuic strategy can preserve users' privacy and antimoney launder.

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. As an important part of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in military, transportation, medical, and household fields. However, in the applications of wireless sensor networks, the adversary can infer the location of a source node and an event by backtracking attacks and traffic analysis. The location privacy leakage of a source node has become one of the most urgent problems to be solved in wireless sensor networks. To solve the problem of source location privacy leakage, in this paper, we first propose a proxy source node selection mechanism by constructing the candidate region. Secondly, based on the residual energy of the node, we propose a shortest routing algorithm to achieve better forwarding efficiency. Finally, by combining the proposed proxy source node selection mechanism with the proposed shortest routing algorithm based on the residual energy, we further propose a new, anonymous communication scheme. Meanwhile, the performance analysis indicates that the anonymous communication scheme can effectively protect the location privacy of the source nodes and reduce the network overhead.

The development of Internet of Things (IoT) and Mobile Edge Computing (MEC) has led to close cooperation between electronic devices. It requires strong reliability and trustworthiness of the devices involved in the communication. However, current trust mechanisms have the following issues: (1) heavily relying on a trusted third party, which may incur severe security issues if it is corrupted, and (2) malicious evaluations on the involved devices which may bias the trustrank of the devices. By introducing the concepts of risk management and blockchain into the trust mechanism, we here propose a blockchain-based trust mechanism for distributed IoT devices in this paper. In the proposed trust mechanism, trustrank is quantified by normative trust and risk measures, and a new storage structure is designed for the domain administration manager to identify and delete the malicious evaluations of the devices. Evidence shows that the proposed trust mechanism can ensure data sharing and integrity, in addition to its resistance against malicious attacks to the IoT devices.