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Existing practical frameworks like the matrix mechanism do not provide such fine-grained control (they optimize\n            <jats:italic>total<\/jats:italic>\n            error, which allows some query answers to be more accurate than necessary, at the expense of other queries that become no longer useful). Thus, we design a fitness-for-use strategy that adds privacy-preserving Gaussian noise to query answers. The covariance structure of the noise is optimized to meet the fine-grained accuracy requirements while minimizing the cost to privacy.\n          <\/jats:p>","DOI":"10.14778\/3467861.3467864","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T16:17:12Z","timestamp":1635265032000},"page":"1730-1742","source":"Crossref","is-referenced-by-count":7,"title":["Optimizing fitness-for-use of differentially private linear queries"],"prefix":"10.14778","volume":"14","author":[{"given":"Yingtai","family":"Xiao","sequence":"first","affiliation":[{"name":"Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Ding","sequence":"additional","affiliation":[{"name":"Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Kifer","sequence":"additional","affiliation":[{"name":"Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3226070"},{"key":"e_1_2_1_3_1","volume-title":"Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. 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