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Consequently, achieving a comprehensive understanding of the design, implementation, and efficiency of the DP algorithms within the ML domain is imperative. This survey provides a systematic review of DP methods across ML approaches, including traditional ML, federated learning, and deep learning. Through a thematic analysis of 106 studies, we identify key DP implementation strategies, examine their impact on model performance, and highlight the advantages and limitations of existing approaches. Our findings offer practical insights to assist researchers and practitioners in selecting appropriate DP mechanisms based on specific requirements. Finally, we discuss open challenges and future research directions to advance DP techniques for improved privacy-utility trade-offs in ML applications.<\/jats:p>","DOI":"10.1145\/3800684","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T21:11:22Z","timestamp":1773090682000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Systematic Literature Review on Differential Privacy in Machine Learning"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9921-1630","authenticated-orcid":false,"given":"Samsad","family":"Jahan","sequence":"first","affiliation":[{"name":"Victoria University","place":["Melbourne, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5955-6295","authenticated-orcid":false,"given":"Yongfeng","family":"Ge","sequence":"additional","affiliation":[{"name":"Victoria University","place":["Melbourne, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4029-7051","authenticated-orcid":false,"given":"Elisa","family":"Bertino","sequence":"additional","affiliation":[{"name":"Computer Science, Purdue University","place":["West Lafayette, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6157-2753","authenticated-orcid":false,"given":"Enamul","family":"Kabir","sequence":"additional","affiliation":[{"name":"University of Southern Queensland","place":["Toowoomba, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9285-2419","authenticated-orcid":false,"given":"Hasan","family":"Mahmud","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology","place":["Rochester, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-9215","authenticated-orcid":false,"given":"Zhonglong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Zhejiang Normal University","place":["Jinhua, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-0996","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"Victoria University","place":["Melbourne, Australia"]}]}],"member":"320","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3526934","article-title":"A framework for privacy-preserving in IoV using federated learning with differential privacy","author":"Adnan Muhammad","year":"2025","unstructured":"Muhammad Adnan, Madiha Haider Syed, Adeel Anjum, and Semeen Rehman. 2025. 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