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However, the existing design of these systems does not distinguish data analysts of different privilege levels or trust levels. This design can have an unfair apportion of the privacy budget among the data analyst if treating them as a single entity, or waste the privacy budget if considering them as non-colluding parties and answering their queries independently. In this paper, we propose DProvDB, a fine-grained privacy provenance framework for the multi-analyst scenario that tracks the privacy loss to each single data analyst. Under this framework, when given a fixed privacy budget, we build algorithms that maximize the number of queries that could be answered accurately and apportion the privacy budget according to the privilege levels of the data analysts.<\/jats:p>","DOI":"10.1145\/3626761","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T19:01:21Z","timestamp":1702407681000},"page":"1-27","source":"Crossref","is-referenced-by-count":5,"title":["DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0983-2730","authenticated-orcid":false,"given":"Shufan","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Waterloo, Waterloo, ON, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4999-4937","authenticated-orcid":false,"given":"Xi","family":"He","sequence":"additional","affiliation":[{"name":"University of Waterloo, Waterloo, ON, Canada"}]}],"member":"320","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Plume: Differential Privacy at Scale. 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