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When synthetic data is generated, users may be interested in knowing if their aggregated queries generating such statistics can be reliably answered on the synthetic data, for instance, to decide if the synthetic data is suitable for specific tasks. However, the standard data generation systems do not provide \"per-query\" quality guarantees on the synthetic data, and the users have no way of knowing how much the aggregated statistics on the synthetic data can be trusted. To address this problem, we present a novel framework named<jats:italic>DP-PQD (differentially-private per-query decider)<\/jats:italic>to detect if the query answers on the private and synthetic datasets are within a user-specified threshold of each other while guaranteeing differential privacy. We give a suite of private algorithms for per-query deciders for count, sum, and median queries, analyze their properties, and evaluate them experimentally.<\/jats:p>","DOI":"10.14778\/3617838.3617844","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T17:07:41Z","timestamp":1701709661000},"page":"65-78","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["DP-PQD: Privately Detecting Per-Query Gaps in Synthetic Data Generated by Black-Box Mechanisms"],"prefix":"10.14778","volume":"17","author":[{"given":"Shweta","family":"Patwa","sequence":"first","affiliation":[{"name":"Duke University"}]},{"given":"Danyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Duke University"}]},{"given":"Amir","family":"Gilad","sequence":"additional","affiliation":[{"name":"Hebrew University"}]},{"given":"Ashwin","family":"Machanavajjhala","sequence":"additional","affiliation":[{"name":"Duke University"}]},{"given":"Sudeepa","family":"Roy","sequence":"additional","affiliation":[{"name":"Duke University"}]}],"member":"320","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2022. 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