Wei, Zhongkai ORCID: https://orcid.org/0009-0001-1701-1043, Su, Ye ORCID: https://orcid.org/0000-0002-4912-3197, Zhang, Xi ORCID: https://orcid.org/0000-0001-7237-3656, Yang, Haining ORCID: https://orcid.org/0000-0002-1958-3117, Qin, Jing ORCID: https://orcid.org/0000-0003-2380-0396 and Ma, Jixin ORCID: https://orcid.org/0000-0001-7458-7412 (2025) Machine Learning meets Encrypted Search: the impact and efficiency of OMKSA in data security. International Journal of Intelligent Systems, 2025:2429577. ISSN 0884-8173 (Print), 1098-111X (Online) (doi:10.1155/int/2429577)

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	authorization, machine learning, oblivious transfer, searchable encryption
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	20 Jan 2025 17:12
URI:	http://gala.gre.ac.uk/id/eprint/49476

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