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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>Recommendation systems powered by artificial intelligence (AI) are widely used to improve user experience. However, AI inevitably raises privacy leakage and other security issues due to the utilization of extensive user data. Addressing these challenges can protect users\u2019 personal information, benefit service providers, and foster service ecosystems. Presently, numerous techniques based on differential privacy have been proposed to solve this problem. However, existing solutions encounter issues such as inadequate data utilization and a tenuous trade-off between privacy protection and recommendation effectiveness. To enhance recommendation accuracy and protect users\u2019 private data, we propose ID-SR, a novel privacy-preserving social recommendation scheme for trustworthy AI based on the infinite divisibility of Laplace distribution. We first introduce a novel recommendation method adopted in ID-SR, which is established based on matrix factorization with a newly designed social regularization term for improving recommendation effectiveness. We then propose a differential privacy-preserving scheme tailored to the above method that leverages the Laplace distribution\u2019s characteristics to safeguard user data. Theoretical analysis and experimentation evaluation on two publicly available datasets demonstrate that our scheme achieves a superior balance between privacy protection and recommendation effectiveness, ultimately delivering an enhanced user experience.<\/jats:p>","DOI":"10.1145\/3639412","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T21:58:31Z","timestamp":1704232711000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["ID-SR: Privacy-Preserving Social Recommendation Based on Infinite Divisibility for Trustworthy AI"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3055-7785","authenticated-orcid":false,"given":"Jingyi","family":"Cui","sequence":"first","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8701-3944","authenticated-orcid":false,"given":"Guangquan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9104-2975","authenticated-orcid":false,"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7720-7165","authenticated-orcid":false,"given":"Shicheng","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7091-0910","authenticated-orcid":false,"given":"Jianli","family":"Wang","sequence":"additional","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0586-7132","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Jinhua, China and School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9288-2754","authenticated-orcid":false,"given":"Shihui","family":"Fu","sequence":"additional","affiliation":[{"name":"TU Delft, Delft, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1525-2342","authenticated-orcid":false,"given":"Zhaohua","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China and School of CyberSpace Security, Hainan University, Haikou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2572-2355","authenticated-orcid":false,"given":"Xi","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5944-2714","authenticated-orcid":false,"given":"Shaoying","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Informatics and Data Science, Hiroshima University, Higashihiroshima, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"issue":"23","key":"e_1_3_2_2_2","first-page":"10","article-title":"Deep learning with Gaussian differential privacy","volume":"2020","author":"Bu Zhiqi","year":"2020","unstructured":"Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J. 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