Aiming at reducing the local storage burden and computational costs, numerous individuals and enterprises are willing to outsource their data to the cloud server. Meanwhile, due to the loss of the actual physical control over their data files once outsourced to the cloud server, how to guarantee the cloud server keep user’s data integrity is an important security issue to be addressed urgently. Accordingly, multiple data integrity checking schemes based on the traditional cryptosystem have been proposed. However, with the advent and development of quantum computer, these existing data integrity checking schemes are no longer secure. Thus, it is necessary to study the new scheme which can resist quantum attack to adapt to the quantum era. In this work, we put forward a novel scheme named lattice-based privacy-preserving and forward-secure cloud storage public auditing scheme (LB-PPFS). Our proposed scheme is not only quantum-attack-against, but also enjoy the privacy-preserving and forward-secure property. In the proposed scheme, a curious auditor cannot learn any knowledge of user’s data because the original data is encapsulated with a random number. In addition, the lattice basis delegation technique is adopted to achieve forward security for resisting key exposure attack. Based on the hardness assumptions of SIS problem from lattice, we prove that the proposed scheme can achieve formally provable security. Besides, the theoretical analysis and performance evaluation demonstrate that the proposed scheme is effective and feasible to guarantee the quantum security for the data integrity in cloud storage.

A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words. The extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and leverage extraction results. Experiment results show that the proposed model improves the extraction of five types of TCM clinical terms, including traditional Chinese medicine, symptoms, patterns, diseases and formulas. The best F1-value of the experiment reaches 78.70% on the test dataset. This method can reduce the cost of manual labeling and improve the result in the NER research of TCM clinical terms.

In this letter, we discuss the security weakness of Wang et al. ’s attribute-based data sharing scheme, in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (TIFS) (DOI: 10.1109/TIFS.2016.2549004). Through designing two concrete attacks, we identify two serious security flaws in their scheme. 1) First, we show that their scheme is insecure because in their scheme any authenticated user can freely tamper with the weight of his own attribute to gain higher level decryption privilege to arbitrarily decrypt the ciphertext belonging to another user with higher weight of attribute. 2) Second, we further demonstrate that their scheme is trivial insecure because in their scheme even any malicious authenticated user’s attribute does not match the access policy of a ciphertext, he/she still has the power to decrypt the ciphertext, i.e., the decryption power is independent of attributes, thus, their scheme is not a rigorous attribute-based scheme. The two weaknesses discovered may hinder their scheme infeasible for practical deployment. Accordingly, we present a remedy solution to the issues while preserving all the security features of the original scheme. We hope that our cryptoanalysis and remedy scheme may contribute to avoiding similar design flaws in future designs.

The smart grid, as a crucial part of modern energy systems, handles extensive and diverse data, including inputs from various sensors, metering devices, and user interactions. Outsourcing data storage to remote cloud servers presents an economical solution for enhancing data management within the smart grid ecosystem. However, ensuring data privacy before transmitting it to the cloud is a critical consideration. Therefore, it is common practice to encrypt the data before uploading them to the cloud. While encryption provides data confidentiality, it may also introduce potential issues such as limiting data owners’ ability to query their data. The searchable attribute-based encryption (SABE) not only enables fine-grained access control in a dynamic large-scale environment but also allows for data searches on the ciphertext domain, making it an effective tool for cloud data sharing. Although SABE has become a research hotspot, existing schemes often have limitations in terms of computing efficiency on the client side, weak security of the ciphertext and the trapdoor. To address these issues, we propose an efficient server-aided ciphertext-policy searchable attribute-based encryption scheme (SA-CP-SABE). In SA-CP-SABE, the user’s data access authority is consistent with the search authority. During the search process, calculations are performed not only to determine whether the ciphertext matches the keyword in the trapdoor, but also to assist subsequent user ciphertext decryption by reducing computational complexity. Our scheme has been proven under the random oracle model to achieve the indistinguishability of the ciphertext and the trapdoor and to resist keyword-guessing attacks. Finally, the performance analysis and simulation of the proposed scheme are provided, and the results show that it performs with high efficiency.

 As a kind of retrieval tool, the thesaurus plays an important role in the field of information retrieval. The compilation of thesaurus is beneficial to academic exchanges by scholars from different backgrounds. Based on the early survey, which discovers 423 Chinese thesauri and 126 English thesauri, this paper statistically analyzes the thesaurus from the publishing years, disciplinary fields, volume of words, organizations and semantic relations. The standards, the compiling techniques and the development of the indexing language are summarized respectively. All the above provides references for future work.