{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T16:15:00Z","timestamp":1784823300780,"version":"3.55.0"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"09","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p> As the usage of deep learning applications continues to proliferate, concerns surrounding the privacy and security of deep learning models have intensified. Recent research has pointed out that deep learning models are vulnerable to various privacy attacks, with Membership Inference Attacks (MIAs) being particularly prevalent. MIA can compromise privacy by revealing the presence of unknown data points in a model\u2019s training set, posing a significant threat especially when the training data contains sensitive information. To this end, scholars have proposed many defense strategies, but striking a balance between protecting model privacy and maintaining utility remains a difficult challenge. In this paper, we propose a defense method against membership inference attacks, called RFE-KD, which integrates feature extraction technology and knowledge distillation technology. Extensive experiments show that our approach significantly reduces the accuracy of membership inference attacks without sacrificing model accuracy, consistently outperforming the most existing defense methods. <\/jats:p>","DOI":"10.1142\/s0218001424500204","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T01:43:54Z","timestamp":1722476634000},"source":"Crossref","is-referenced-by-count":1,"title":["PFE-KD: A Defense Method Against Membership Inference Attack Without Loss of Accuracy"],"prefix":"10.1142","volume":"39","author":[{"given":"Hao","family":"Liu","sequence":"first","affiliation":[{"name":"Advertising School, Communication University of China, Beijing 100024, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyao","family":"He","sequence":"additional","affiliation":[{"name":"Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Massachusetts, Boston, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"S0218001424500204BIB001","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"S0218001424500204BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2339736"},{"key":"S0218001424500204BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17394-3"},{"key":"S0218001424500204BIB004","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"S0218001424500204BIB005","first-page":"267","volume-title":"28th USENIX Security Symposium (USENIX Security 19)","author":"Carlini N.","year":"2019"},{"key":"S0218001424500204BIB006","doi-asserted-by":"publisher","DOI":"10.1142\/9789811232701_0003"},{"key":"S0218001424500204BIB008","first-page":"1964","volume-title":"Int. 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