{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T18:45:48Z","timestamp":1774982748358,"version":"3.50.1"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hong Kong RGC Research Fund"},{"name":"NTU-NAP start up funding"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2024,12,18]]},"abstract":"<jats:p>\n                    In the standard model of differential privacy (DP), every user's privacy is treated equally, which is captured by a single privacy parameter \\varepsilon. However, in many real-world situations, users may have diverse privacy concerns and requirements, some conservative while others liberal. This is formalized by the model of\n                    <jats:italic toggle=\"yes\">personalized differential privacy (PDP),<\/jats:italic>\n                    where each user may have a different privacy parameter \\varepsilon. However, existing techniques for PDP cannot provide good utility for many fundamental problems such as basic counting and sum estimation. In this paper, we present the\n                    <jats:italic toggle=\"yes\">personalized truncation mechanism<\/jats:italic>\n                    for these problems under PDP. We first show that, theoretically, it is never worse than previous mechanisms (up to polylogarithmic factors) on any instance, while can be much better in certain cases. Then we use extensive experiments on both real and synthetic data to demonstrate its empirical advantages. Our mechanism also works for user-level DP, thus supporting a large class of SJA queries over relational databases under foreign-key constraints.\n                  <\/jats:p>","DOI":"10.1145\/3698825","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T16:40:35Z","timestamp":1734712835000},"page":"1-25","source":"Crossref","is-referenced-by-count":2,"title":["Personalized Truncation for Personalized Privacy"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8277-1807","authenticated-orcid":false,"given":"Dajun","family":"Sun","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, HK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0394-4125","authenticated-orcid":false,"given":"Wei","family":"Dong","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3488-6386","authenticated-orcid":false,"given":"Yuan","family":"Qiu","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, HK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2178-3716","authenticated-orcid":false,"given":"Ke","family":"Yi","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, HK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2005.22"},{"key":"e_1_2_1_3_1","volume-title":"Heterogeneous differential privacy. arXiv preprint arXiv:1504.06998","author":"Alaggan Mohammad","year":"2015","unstructured":"Mohammad Alaggan, S\u00e9bastien Gambs, and Anne-Marie Kermarrec. Heterogeneous differential privacy. arXiv preprint arXiv:1504.06998, 2015."},{"key":"e_1_2_1_4_1","volume-title":"Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. Advances in neural information processing systems, 33:14106--14117","author":"Asi Hilal","year":"2020","unstructured":"Hilal Asi and John C Duchi. Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. 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Foundations and Trends\u00ae in Theoretical Computer Science, 9(3--4):211--407","author":"Dwork Cynthia","year":"2014","unstructured":"Cynthia Dwork, Aaron Roth, et al. The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science, 9(3--4):211--407, 2014."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.v1i2.570"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2775051.2677005"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560567"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2007.1062"},{"key":"e_1_2_1_20_1","first-page":"25993","article-title":"Instance-optimal mean estimation under differential privacy","volume":"34","author":"Huang Ziyue","year":"2021","unstructured":"Ziyue Huang, Yuting Liang, and Ke Yi. Instance-optimal mean estimation under differential privacy. Advances in Neural Information Processing Systems, 34:25993--26004, 2021.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3187009.3177733"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2015.7113353"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36594-2_26"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342274"},{"key":"e_1_2_1_25_1","unstructured":"Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford large network dataset collection. http:\/\/snap.stanford.edu\/ data 2014."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57454-7_48"},{"key":"e_1_2_1_27_1","volume-title":"l-diversity: Privacy beyond k-anonymity. 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In https:\/\/drive.google.com\/file\/d\/1gsgBTSeXCQLiBsSce1KtwyrGm-8blVuq\/view?usp=sharing."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2015.06.014"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142500"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-020-0103-0"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/WAIM.2008.22"},{"issue":"4","key":"e_1_2_1_38_1","first-page":"655","article-title":"A utility-optimized framework for personalized private histogram estimation","volume":"31","author":"Yiwen NIE","year":"2018","unstructured":"NIE Yiwen, Wei Yang, Liusheng Huang, Xike Xie, Zhenhua Zhao, and Shaowei Wang. A utility-optimized framework for personalized private histogram estimation. IEEE Transactions on Knowledge and Data Engineering, 31(4):655--669, 2018.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.14778\/1921071.1921080"},{"key":"e_1_2_1_40_1","volume-title":"Dprovdb: Differentially private query processing with multi-analyst provenance. arXiv preprint arXiv:2309.10240","author":"Zhang Shufan","year":"2023","unstructured":"Shufan Zhang and Xi He. Dprovdb: Differentially private query processing with multi-analyst provenance. arXiv preprint arXiv:2309.10240, 2023."}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698825","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3698825","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T17:47:05Z","timestamp":1774979225000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698825"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12,18]]}},"alternative-id":["10.1145\/3698825"],"URL":"https:\/\/doi.org\/10.1145\/3698825","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,18]]}}}