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Bayesian differential privacy (BDP) extends DP to account for these correlations, yet current BDP mechanisms indicate a notable utility loss, limiting its adoption.<\/jats:p>\n          <jats:p>In this work, we address whether BDP can be realistically implemented in common data structures without sacrificing utility\u2014a key factor for its applicability. By analyzing arbitrary and structured correlation models, including Gaussian multivariate distributions and Markov chains, we derive practical utility guarantees for BDP. Our contributions include theoretical links between DP and BDP and a novel methodology to adapt DP mechanisms to meet the requirements of BDP. Through evaluations on real-world databases, we demonstrate that our novel theorems enable the design of BDP mechanisms that maintain competitive utility, paving the way for practical privacy-preserving data practices in correlated settings.<\/jats:p>","DOI":"10.14778\/3749646.3749679","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T17:55:06Z","timestamp":1757008506000},"page":"4090-4103","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Balancing Privacy and Utility in Correlated Data: A Study of Bayesian Differential Privacy"],"prefix":"10.14778","volume":"18","author":[{"given":"Martin","family":"Lange","sequence":"first","affiliation":[{"name":"Karlsruhe Institute of Technology, KASTEL SRL"}]},{"given":"Patricia","family":"Guerra-Balboa","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, KASTEL SRL"}]},{"given":"Javier","family":"Parra-Arnau","sequence":"additional","affiliation":[{"name":"Universitat Polit\u00e8cnica de Catalunya"}]},{"given":"Thorsten","family":"Strufe","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, KASTEL SRL"}]}],"member":"320","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1093\/bioinformatics\/btz837"},{"doi-asserted-by":"publisher","key":"e_1_2_1_2_1","DOI":"10.1145\/2508859.2516735"},{"doi-asserted-by":"publisher","key":"e_1_2_1_3_1","DOI":"10.1145\/2018536.2018544"},{"doi-asserted-by":"publisher","key":"e_1_2_1_4_1","DOI":"10.1007\/978-3-322-90157-6"},{"doi-asserted-by":"publisher","key":"e_1_2_1_5_1","DOI":"10.1145\/2450142.2450148"},{"doi-asserted-by":"publisher","key":"e_1_2_1_6_1","DOI":"10.1111\/j.1539-6924.1992.tb00674.x"},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.1109\/sp46214.2022.9833649"},{"unstructured":"Darshan Chakrabarti Jie Gao Aditya Saraf Grant Schoenebeck and Fang-Yi Yu. 2022. 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