The convergence of machine learning and searchable encryption enhances the ability to protect the privacy and security of data and enhances the processing power of confidential data. To enable users to efficiently perform machine learning tasks on encrypted data domains, we delve into oblivious keyword search with authorization (OKSA). The OKSA scheme effectively maintains the privacy of the user’s query keywords and prevents the cloud server from inferring ciphertext information through the searching process. However, limitations arise because the traditional OKSA approach does not support multi‐keyword searches. If a data file is associated with multiple keywords, each keyword and corresponding data must be encrypted one by one, resulting in inefficiency. We introduce an innovative approach aimed at enhancing the efficiency of search processes while addressing the limitation of current encryption and search systems that handle only a single keyword. This method, known as the oblivious multiple keyword search with authorization (OMKSA), is designed for more effective keyword retrieval. One of our important innovations is that it uses the arithmetic techniques of bilinear pairs to generate new tokens and new search methods to optimize communication efficiency. Moreover, we present a detailed and rigorous demonstration of the security for our proposed protocol, aligned with the predefined security model. We conducted a comparative experiment to determine which of the two schemes, OKSA and OMKSA, is more efficient when querying multiple keywords. Based on our experimental results, our OMKSA is very efficient for data searchers. As the number of query keywords increases, the computational overhead of connected keyword searches remains stable. Finally, as we move into the 5G era, the potential applications of OMKSA are huge, with clear implications for areas such as machine learning and artificial intelligence. Our findings pave the way for further exploration and deployment of these frontier areas.

Cloud computing has become prevalent due to its extensive storage resources and robust computational capacities. To protect data security and privacy, data owners opt for uploading encrypted data to the cloud. Flexible sharing of these encrypted data in a group of users is a critical functionality in cloud storage. In addition, given that users may exit the group, revocation becomes a crucial requirement in group data-sharing systems. The Key-Aggregate Cryptosystem (KAC) has become a promising mechanism for group data sharing. The decryption rights for any set of ciphertexts can be efficiently delegated by distributing a constant-size aggregate key, while the confidentiality of other ciphertexts outside the set is maintained. However, in previous KAC schemes, revocation remains a challenging task regarding key update, ciphertext re-encryption, and collision resistance. In this paper, we propose a Key-Aggregate Cryptosystem with User Revocation (KAC-UR) scheme to overcome this challenge. The KAC-UR scheme not only achieves flexible data sharing, but also can perform secure and efficient user revocation with properties including collision resistance, revocation without data owner-user communication, and constant ciphertext size. The KAC-UR scheme also enables the cloud server to perform partial decryption, thereby significantly alleviating the computational burden for users. The KAC-UR scheme is chosen plaintext attack secure under the decisional Bilinear Diffie-Hellman Exponent assumption.

Wildcard searchable encryption is an advanced variant of searchable encryption that can simultaneously maintain the searchability and confidentiality of the encrypted data. The wildcard searchable encryption outperforms the standard one for the fact that the users can use it to search the desired data even with the inexact keywords. Considering the millisecond level response time in the era of 5G, there are higher demands on the efficiency and accuracy that may be a pair of contradictions in wildcard searchable encryption. To improve the efficiency without sacrificing the accuracy, we put forward a novel scheme, tree-based index scheme (TBIS), through filtering the search results step by step instead of enumeration in the prior works and in the instantiation of TBIS, the search time drops sharply to the millisecond level. By using more kinds of characters, the accuracy of search result is improved visibly. TBIS achieves nonadaptive security that is indistinguishable against chosen character set attacks proposed in this paper. The security criteria can capture the relationship among characters, keywords and documents. At last, we put forward a frame structure in machine learning as an application of the proposed scheme.