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This ambivalence raises the question of whether such image data can be used in a privacy-responsible manner. Encryption-based solutions assume the end server can be trusted with keeping data safe; data leaks show us this assumption does not necessarily hold true. Traditional image obfuscation methods such as pixelization or blurring on the other hand fail to offer both sufficient privacy and utility. As such, privacy approaches that can provide privacy protection directly on the data itself while retaining practical utility are required. We here extend two such notions, differential privacy and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            -anonymity, to image data, and extensively evaluate the resulting privacy-utility tradeoff on cross-camera person re-identification and attribute recognition data. Our results show that our proposed approaches can significantly reduce the privacy-sensitivity of image data at source while retaining decent utility for vision-based smart city applications.\n          <\/jats:p>","DOI":"10.1145\/3743680","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T22:26:38Z","timestamp":1749594398000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Differential Privacy and\n            <i>k<\/i>\n            -Anonymity for Pedestrian Image Data: Impact on Cross-Camera Person Re-Identification and Demographic Predictions"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9426-219X","authenticated-orcid":false,"given":"Lucas","family":"Maris","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3135-4915","authenticated-orcid":false,"given":"Yuki","family":"Matsuda","sequence":"additional","affiliation":[{"name":"Okayama University, Okayama, Japan and RIKEN Center for Advanced Intelligence Project, Chuo-ku, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1579-3237","authenticated-orcid":false,"given":"Keiichi","family":"Yasumoto","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan  and RIKEN Center for Advanced Intelligence Project, Chuo-ku, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3226070"},{"key":"e_1_3_2_4_2","first-page":"901","volume-title":"31st International Conference on Very Large Data Bases (VLDB \u201905). 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