With the advent of large-scale social networks, two communication users need to generate session keys with the help of a remote server to communicate securely. In the existing three-party authenticated key exchange (3PAKE) protocols, users’ passwords need to be stored on the server; it cannot resist the server disclosure attack. To solve this security problem, we propose a more efficient 3PAKE protocol based on the verification element by adopting a public-key cryptosystem and approximate smooth projection hash (ASPH) function on an ideal lattice. Using the structure of separating authentication from the server, the user can negotiate the session key only after two rounds of communication. The analysis results show that it can improve the efficiency of computation and communication and resist the server disclosure attack, quantum algorithm attack, and replay attack; moreover, it has session key privacy to the server. This protocol can meet the performance requirement of the current communication network.

Recently, security policies and behaviour detection methods have been proposed to improve the security of blockchain by many researchers. However, these methods cannot discover the source of typical behaviours, such as the malicious applications in the blockchain environment. Android application is an important part of the blockchain operating environment, and machine learning-based Android malware application detection method is significant for blockchain user security. The way of constructing features in these methods determines the performance. The single-feature mechanism, training classifiers with one type of features, cannot detect the malicious applications effectively which exhibit the typical behaviours in various forms. The multifeatures fusion mechanism, constructing mixed features from multiple types of data sources, can cover more kinds of information. However, different types of data sources will interfere with each other in the mixed features constructed by this mechanism. That limits the performance of the model. In order to improve the detection performance of Android malicious applications in complex scenarios, we propose an Android malicious application detection method which includes parallel feature processing and decision mechanism. Our method uses RGB image visualization technology to construct three types of RGB image which are utilized to train different classifiers, respectively, and a decision mechanism is designed to fuse the outputs of subclassifiers through weight analysis. This approach simultaneously extracts different types of features, which preserve application information comprehensively. Different classifiers are trained by these features to guarantee independence of each feature and classifier. On this basis, a comprehensive analysis of many methods is performed on the Android malware dataset, and the results show that our method has better efficiency and adaptability than others.

The existing lattice-based cut-and-choose oblivious transfer protocol is constructed based on the learning-with-errors (LWE) problem, which generally has the problem of inefficiency. An efficient cut-and-choose oblivious transfer protocol is proposed based on the difficult module-learning-with-errors (MLWE) problem. Compression and decompression techniques are introduced in the LWE-based dual-mode encryption system to improve it to an MLWE-based dual-mode encryption framework, which is applied to the protocol as an intermediate scheme. Subsequently, the security and efficiency of the protocol are analysed, and the security of the protocol can be reduced to the shortest independent vector problem (SIVP) on the lattice, which is resistant to quantum attacks. Since the whole protocol relies on the polynomial ring of elements to perform operations, the efficiency of polynomial modulo multiplication can be improved by using fast Fourier transform (FFT). Finally, this paper compares the protocol with an LWE-based protocol in terms of computational and communication complexities. The analysis results show that the protocol reduces the computation and communication overheads by at least a factor of n while maintaining the optimal number of communication rounds under malicious adversary attacks.

Machine learning (ML) and privacy protection are inseparable. On the one hand, ML can be the target of privacy protection; on the other hand, it can also be used as an attack tool for privacy protection. Ring signature (RS) is an effective way for privacy protection in cryptography. In particular, lattice-based RS can still protect the privacy of users even in the presence of quantum computers. However, most current lattice-based RS schemes are based on a strong trapdoor like hash-and-sign, and in such constructions, there is a hidden algebraic structure, that is, added to lattice so that the trapdoor shape is not leaked, which greatly affects the computational efficiency of RS. In this study, utilizing Lyubashevsky collision-resistant hash function over lattice, we construct an RS scheme without trapdoors based on ideal lattice via Fiat‒Shamir with aborts (FSwA) protocol. Regarding security, the proposed scheme satisfies unconditional anonymity against chosen setting attacks (UA-CSA), which is stronger than anonymity against full key exposure (anonymity-FKE), and moreover, our scheme satisfies unforgeability with respect to insider corruption (EU-IC). Regarding computational overhead, compared with other RS schemes that satisfy the same degree of security, our scheme has the highest computational efficiency, the signing and verification time costs of the proposed scheme are obviously better than those of other lattice-based RS schemes without trapdoors, which is more suitable for ML scenarios.

Most current broadcast encryption with optimal parameters is limited to Nick’s class 1 (NC1) circuits and does not support polynomial‐depth circuits (P‐depth circuits), making it difficult to provide flexible access control in broadcast channels among vast user groups. To address this problem, we propose a ciphertext‐policy attribute–based encryption (CP‐ABE) that supports P‐depth circuits on lattices, achieving fully collusion resistance with randomization via the matrix tensors, thereby, making it impossible for unauthorized users to get any details about the plaintext even though they join forces and reducing the security to the evasive learning with errors (evasive LWE). By using matrix tensor–based randomization and evasive LWE, we achieve a new optimal broadcast encryption scheme based on lattice specifically designed to support P‐depth circuits. Since the matrices we choose as tensors have a low‐norm block diagonal structure, the use of evasive LWE is sufficient to ensure security for our scheme. Compared with similar studies, it not only avoids being involved with low‐norm matrices that restrict the system to NC1 circuits, but also eliminates the need for an additional assumption of the unproven tensor LWE. In addition, the use of matrix tensors further expands the dimensionality, which in turn enables the encryption of bit strings rather than a single bit, significantly reducing ciphertext expansion. Meanwhile, the CP‐ABE that we use to achieve the broadcast encryption scheme has a more compact ciphertext with a parameter size of O ( m ² · d ).