{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T02:07:41Z","timestamp":1768615661521,"version":"3.49.0"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? We propose to demonstrate DPXPlain, the first system for explaining group-by aggregate query answers with DP. DPXPlain allows users to compare values of two groups and receive a validity check, and further provides an explanation table with an interactive visualization, containing the approximately 'top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps.<\/jats:p>","DOI":"10.14778\/3611540.3611596","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:37Z","timestamp":1694777557000},"page":"3962-3965","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Explaining Differentially Private Query Results with DPXPlain"],"prefix":"10.14778","volume":"16","author":[{"given":"Tingyu","family":"Wang","sequence":"first","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchao","family":"Tao","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Gilad","sequence":"additional","affiliation":[{"name":"Hebrew University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashwin","family":"Machanavajjhala","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudeepa","family":"Roy","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53641-4_24"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517844"},{"key":"e_1_2_1_3_1","volume-title":"UCI machine learning repository","author":"Dua D.","year":"2017","unstructured":"D. Dua and C. Graff. UCI machine learning repository, 2017."},{"key":"e_1_2_1_4_1","volume-title":"One-shot dp top-k mechanisms. DifferentialPrivacy.org, 08","author":"Durfee D.","year":"2021","unstructured":"D. Durfee and R. Rogers. One-shot dp top-k mechanisms. DifferentialPrivacy.org, 08 2021. https:\/\/differentialprivacy.org\/one-shot-top-k\/."},{"key":"e_1_2_1_5_1","first-page":"3527","volume-title":"NeurIPS","author":"Durfee D.","year":"2019","unstructured":"D. Durfee and R. M. Rogers. Practical differentially private top-k selection with pay-what-you-get composition. In NeurIPS, pages 3527--3537, 2019."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_7_1","volume-title":"The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci., 9(3--4):211--407","author":"Dwork C.","year":"2014","unstructured":"C. Dwork, A. Roth, et al. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. 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