{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:10:36Z","timestamp":1775538636655,"version":"3.50.1"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"DOI":"10.13039\/501100001475","name":"Nanyang Technological University","doi-asserted-by":"publisher","award":["24584-00001"],"award-info":[{"award-number":["24584-00001"]}],"id":[{"id":"10.13039\/501100001475","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education, Singapore","doi-asserted-by":"crossref","award":["RG19\/25"],"award-info":[{"award-number":["RG19\/25"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,12,4]]},"abstract":"<jats:p>\n                    Differential Privacy (DP) is a widely adopted standard for privacy-preserving data analysis, but it assumes a uniform privacy budget across all records, limiting its applicability when privacy requirements vary with data values. Per-record Differential Privacy (PrDP) addresses this by defining the privacy budget as a function of each record, offering better alignment with real-world needs. However, the dependency between the privacy budget and the data value introduces challenges in protecting the budget's privacy itself. Existing solutions either handle specific privacy functions or adopt relaxed PrDP definitions. A simple workaround is to use the global minimum of the privacy function, but this severely degrades utility, as the minimum is often set extremely low to account for rare records with high privacy needs. In this work, we propose a general and practical framework that enables any standard DP mechanism to support PrDP, with error depending only on the minimal privacy requirement among records actually present in the dataset. Since directly revealing this minimum may leak information, we introduce a core technique called\n                    <jats:italic toggle=\"yes\">privacy-specified domain partitioning<\/jats:italic>\n                    , which ensures accurate estimation without compromising privacy. We also extend our framework to the local DP setting via a novel technique,\n                    <jats:italic toggle=\"yes\">privacy-specified query augmentation<\/jats:italic>\n                    . Using our framework, we present the first PrDP solutions for fundamental tasks such as count, sum, and maximum estimation. Experimental results show that our mechanisms achieve high utility and significantly outperform existing Personalized DP (PDP) methods, which can be viewed as a special case of PrDP with relaxed privacy protection.\n                  <\/jats:p>","DOI":"10.1145\/3769752","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:32:13Z","timestamp":1764995533000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["A General Framework for Per-record Differential Privacy"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8686-359X","authenticated-orcid":false,"given":"Xinghe","family":"Chen","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8277-1807","authenticated-orcid":false,"given":"Dajun","family":"Sun","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8989-9662","authenticated-orcid":false,"given":"Quanqing","family":"Xu","sequence":"additional","affiliation":[{"name":"Oceanbase, Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0394-4125","authenticated-orcid":false,"given":"Wei","family":"Dong","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_2_1_2_1","article-title":"Heterogeneous Differential Privacy","volume":"7","author":"Alaggan Mohammad","year":"2016","unstructured":"Mohammad Alaggan, S\u00e9bastien Gambs, and Anne-Marie Kermarrec. 2016. Heterogeneous Differential Privacy. Journal of Privacy and Confidentiality, Vol. 7, 2 (2016).","journal-title":"Journal of Privacy and Confidentiality"},{"key":"e_1_2_1_3_1","volume-title":"Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. Advances in neural information processing systems","author":"Asi Hilal","year":"2020","unstructured":"Hilal Asi and John C Duchi. 2020. Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. Advances in neural information processing systems, Vol. 33 (2020), 14106-14117."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26951-7_22"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3108463"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2746539.2746632"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","first-page":"19073","DOI":"10.52202\/075280-0836","article-title":"Have it your way: Individualized Privacy Assignment for DP-SGD","volume":"36","author":"Boenisch Franziska","year":"2023","unstructured":"Franziska Boenisch, Christopher M\u00fchl, Adam Dziedzic, Roy Rinberg, and Nicolas Papernot. 2023. Have it your way: Individualized Privacy Assignment for DP-SGD. Advances in Neural Information Processing Systems, Vol. 36 (2023), 19073-19103.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589287"},{"key":"e_1_2_1_9_1","volume-title":"Private and Continual Release of Statistics. ACM Transactions on Information and System Security","author":"Hubert Chan T.-H.","year":"2011","unstructured":"T.-H. Hubert Chan, Elaine Shi, and Dawn Song. 2011. Private and Continual Release of Statistics. ACM Transactions on Information and System Security (2011)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498248"},{"key":"e_1_2_1_11_1","unstructured":"Xinghe Chen Dajun Sun Quanqing Xu and Wei Dong. 2025. A General Framework for Per-record Differential Privacy [Full Version]. (2025). https:\/\/drive.google.com\/drive\/folders\/1U1HDfh8FgyFq1ddsrRJFx8tU9uMgKK0a?usp=sharing"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17653-2_13"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-019-1910-3"},{"key":"e_1_2_1_14_1","unstructured":"Bolin Ding Janardhan Kulkarni and Sergey Yekhanin. 2017. Collecting Telemetry Data Privately. In NeurIPS."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654931"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517844"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3697831"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3690225"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179466"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589268"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3452813"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3517804.3524143"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3631504.3631506"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3584372.3588669"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536466"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1806689.1806787"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2775051.2677005"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560567"},{"key":"e_1_2_1_31_1","first-page":"28080","article-title":"Individual privacy accounting via a renyi filter","volume":"34","author":"Feldman Vitaly","year":"2021","unstructured":"Vitaly Feldman and Tijana Zrnic. 2021. Individual privacy accounting via a renyi filter. Advances in Neural Information Processing Systems, Vol. 34 (2021), 28080-28091.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2007.1062"},{"key":"e_1_2_1_33_1","unstructured":"Ziyue Huang Yuting Liang and Ke Yi. 2021. Instance-optimal Mean Estimation Under Differential Privacy. In NeurIPS."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3187009.3177733"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2015.7113353"},{"key":"e_1_2_1_36_1","unstructured":"Kaggle. 2014. San Francisco City Employee Salary Data. https:\/\/www.kaggle.com\/datasets\/kaggle\/sf-salaries\/data."},{"key":"e_1_2_1_37_1","volume-title":"Japan's 100 million customs trade statistics since","year":"1988","unstructured":"Kaggle. 2020a. Japan's 100 million customs trade statistics since 1988. https:\/\/www.kaggle.com\/datasets\/zanjibar\/100-million-data-csv."},{"key":"e_1_2_1_38_1","volume-title":"Ontario Public Sector Salary","year":"2019","unstructured":"Kaggle. 2020b. Ontario Public Sector Salary 2019. https:\/\/www.kaggle.com\/datasets\/rajacsp\/ontario."},{"key":"e_1_2_1_39_1","unstructured":"Kaggle. 2023. Banking Dataset - Marketing Targets. https:\/\/www.kaggle.com\/datasets\/prakharrathi25\/banking-dataset-marketing-targets."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57454-7_48"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3144690"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3670351"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3725415"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497436"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2010.14"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488825"},{"key":"e_1_2_1_47_1","volume-title":"Scalable Private Learning with PATE. In International Conference on Learning Representations.","author":"Papernot Nicolas","year":"2018","unstructured":"Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ulfar Erlingsson. 2018. Scalable Private Learning with PATE. In International Conference on Learning Representations."},{"key":"e_1_2_1_48_1","first-page":"17335","article-title":"Privately publishable per-instance privacy","volume":"34","author":"Redberg Rachel","year":"2021","unstructured":"Rachel Redberg and Yu-Xiang Wang. 2021. Privately publishable per-instance privacy. Advances in Neural Information Processing Systems, Vol. 34 (2021), 17335-17346.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681989"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/FITME.2009.115"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3698825"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389762"},{"key":"e_1_2_1_53_1","unstructured":"Apple Differential Privacy Team. 2017. Learning with Privacy at Scale. https:\/\/machinelearning.apple.com\/research\/learning-with-privacy-at-scale."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142500"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3725288"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-020-0103-0"},{"key":"e_1_2_1_57_1","volume-title":"Personalized Differential Privacy in the Shuffle Model. In International Conference on Artificial Intelligence Security and Privacy. Springer, 468-482","author":"Yang Ruilin","year":"2023","unstructured":"Ruilin Yang, Hui Yang, Jiluan Fan, Changyu Dong, Yan Pang, Duncan S Wong, and Shaowei Wang. 2023. Personalized Differential Privacy in the Shuffle Model. In International Conference on Artificial Intelligence Security and Privacy. Springer, 468-482."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/WAIM.2008.22"},{"key":"e_1_2_1_59_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. 2018. A utility-optimized framework for personalized private histogram estimation. IEEE Transactions on Knowledge and Data Engineering, Vol. 31, 4 (2018), 655-669.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769752","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T04:28:47Z","timestamp":1775536127000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,4]]},"references-count":59,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12,4]]}},"alternative-id":["10.1145\/3769752"],"URL":"https:\/\/doi.org\/10.1145\/3769752","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,4]]}}}