[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122088-en":3,"doc-seo-122088-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122088,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Meets Encrypted Search - The Impact and Efficiency of OMKSA in Data Security","Machine learning and searchable encryption are combined to strengthen data privacy while enabling efficient analysis on encrypted datasets. The work studies oblivious keyword search with authorization (OKSA), which protects query keywords and prevents cloud inference during search, but suffers from inefficiency because traditional designs do not support multi-keyword queries. To address this limitation, OMKSA introduces a new oblivious multiple keyword search with authorization method using bilinear-pair arithmetic to generate tokens and optimize communication. A security proof and comparative experiments evaluate OKSA versus OMKSA under increasing query keywords, showing stable overhead and high efficiency for multi-keyword search, with promising implications for 5G-era ML and AI.","Wiley  \nInternational Journal of Intelligent Systems Volume 2025, Article ID 2429577, 17 pages [https://doi.org/10.1155/int/2429577](https://doi.org/10.1155/int/2429577)  \nResearch Article  \nMachine Learning Meets Encrypted Search: The Impact and Efficiency of OMKSA in Data Security  \nZhongkai Wei , 1 Ye Su ,2 Xi Zhang ,3,4 Haining Yang , 1 Jing Qin , 1 and Jixin Ma 5  \n1 School of Mathematics, Shandong University, Jinan, China  \n2 School of Information Science and Engineering, Shandong Normal University, Jinan, China  \n3 College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China  \n4 Hebei Provincial Key Laboratory of Network and Information Security, Hebei Normal University, Shijiazhuang, China 5 School of Computing and Mathematical Sciences, University of Greenwich, London, UK  \nCorrespondence should be addressed to Haining Yang; [hainingyang@sdu.edu.cn and Jing Qin](hainingyang@sdu.edu.cn and Jing Qin); [qinjing@sdu.edu.cn](qinjing@sdu.edu.cn)  \nReceived 21 December 2023; Revised 8 June 2024; Accepted 23 December 2024  \nAcademic Editor: Vasudevan Rajamohan  \nCopyright © 2025 Zhongkai Wei et al. International Journal of Intelligent Systems published by John Wiley & Sons Ltd. Tis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nTe convergence of machine learning and searchable encryption enhances the ability to protect the privacy and security of data and enhances the processing power of confdential data. To enable users to efciently perform machine learning tasks on encrypted data domains, we delve into oblivious keyword search with authorization (OKSA) . Te OKSA scheme efectively 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. Ifa data fle is associated with multiple keywords, each keyword and corresponding data must be encrypted one by one, resulting in inefciency. We introduce an innovative approach aimed at enhancing the efciency of search processes while addressing the limitation of current encryption and search systems that handle only a single keyword. Tis method, known as the oblivious multiple keyword search with authorization (OMKSA), is designed for more efective keyword retrieval. One of our important innovations is that it uses the arithmetic techniques ofbilinear pairs to generate new tokens and new search methods to optimize communication efciency. Moreover, we present a detailed and rigorous demonstration of the security for our proposed protocol, aligned with the predefned security model. We conducted a comparative experiment to determine which of the two schemes, OKSA and OMKSA, is more efcient when querying multiple keywords. Based on our experimental results, our OMKSAis very efcient 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 artifcial intelligence. Our fndings pave the way for further exploration and deployment of these frontier areas.  \nKeywords: authorization; machine learning; oblivious transfer; searchable encryption  \n1. Introduction  \nIn contemporary times, the convergence of machine learning (ML) [1, 2] and searchable encryption (SE) [3–5] isan important innovation. Tis combination will promote the enhancement of data privacy protection and the utility of big data. Known for their ability to self-learn and adapt, ML algorithms play a key role in retrieving huge datasets,  \nrecognizing patterns, and predicting outcomes. However, since these algorithms require access to large amounts of d","cbCaicMlMmRcWoZ3","https://ap.wps.com/l/cbCaicMlMmRcWoZ3","pdf",720546,1,17,"English","en",105,"# Introduction\n## Machine learning and searchable encryption overview\n## Privacy risks in public-key searchable encryption\n## Inside keyword guessing attacks\n## Oblivious keyword search and its limitations\n## Proposed direction: OMKSA for multi-keyword queries\n# Method and design\n## OKSA baseline and multi-keyword inefficiency\n## OMKSA construction with bilinear pair techniques\n## Token generation and communication optimization\n# Security analysis\n## Security model and proof for OMKSA\n# Experimental evaluation\n## Comparative efficiency: OKSA vs OMKSA\n## Impact of increasing keyword count\n# Conclusion and applications\n## Implications for 5G, machine learning, and AI","[{\"question\":\"What problem does OKSA face when users search with multiple keywords?\",\"answer\":\"Traditional OKSA does not support multi-keyword searches efficiently, so each keyword and its associated data must be encrypted and processed separately, creating significant overhead.\"},{\"question\":\"How does OMKSA improve efficiency in encrypted multi-keyword search?\",\"answer\":\"OMKSA uses bilinear-pair arithmetic to generate new tokens and new search methods, reducing communication overhead while enabling more effective keyword retrieval.\"},{\"question\":\"What evidence supports the security and performance of OMKSA?\",\"answer\":\"The protocol includes a rigorous security demonstration aligned with a predefined security model, and comparative experiments evaluate OMKSA against OKSA as the number of query keywords increases.\"}]","Machine Learning Meets Encrypted Search - The Impact and Efficiency of OMKSA in Data Security | PDF",1785808761,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-meets-encrypted-search-the-impact-and-efficiency-of-omksa-in-data-security","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-meets-encrypted-search-the-impact-and-efficiency-of-omksa-in-data-security/122088/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does OKSA face when users search with multiple keywords?","Question",{"text":75,"@type":76},"Traditional OKSA does not support multi-keyword searches efficiently, so each keyword and its associated data must be encrypted and processed separately, creating significant overhead.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does OMKSA improve efficiency in encrypted multi-keyword search?",{"text":80,"@type":76},"OMKSA uses bilinear-pair arithmetic to generate new tokens and new search methods, reducing communication overhead while enabling more effective keyword retrieval.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence supports the security and performance of OMKSA?",{"text":84,"@type":76},"The protocol includes a rigorous security demonstration aligned with a predefined security model, and comparative experiments evaluate OMKSA against OKSA as the number of query keywords increases.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]