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Data and Information Quality"],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>\n            An accountable\n            <jats:bold>algorithmic transparency report (ATR)<\/jats:bold>\n            should\n            <jats:italic>ideally<\/jats:italic>\n            investigate (a)\n            <jats:italic>transparency<\/jats:italic>\n            of the underlying algorithm, and (b)\n            <jats:italic>fairness<\/jats:italic>\n            of the algorithmic decisions, and at the same time preserve data subjects\u2019\n            <jats:italic>privacy<\/jats:italic>\n            . However, a provably formal study of the impact to data subjects\u2019 privacy caused by the utility of releasing an ATR (that investigates transparency and fairness), has yet to be addressed in the literature. The far-fetched benefit of such a study lies in the methodical characterization of privacy-utility trade-offs for release of ATRs in public, and their consequential application-specific impact on the dimensions of society, politics, and economics. In this paper, we first investigate and demonstrate potential privacy hazards brought on by the deployment of transparency and fairness measures in released ATRs.\n            <jats:italic>To preserve data subjects\u2019 privacy, we then propose a linear-time optimal-privacy scheme<\/jats:italic>\n            , built upon standard\n            <jats:bold>linear fractional programming (LFP)<\/jats:bold>\n            theory, for announcing ATRs, subject to constraints controlling the tolerance of privacy perturbation on the utility of transparency schemes. Subsequently, we quantify the privacy-utility trade-offs induced by our scheme, and analyze the impact of privacy perturbation on fairness measures in ATRs. To the best of our knowledge, this is the first analytical work that simultaneously addresses trade-offs between the triad of privacy, utility, and fairness, applicable to algorithmic transparency reports.\n          <\/jats:p>","DOI":"10.1145\/3460001","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T13:09:20Z","timestamp":1644584960000},"page":"1-56","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Achieving Transparency Report Privacy in Linear Time"],"prefix":"10.1145","volume":"14","author":[{"given":"Chien-Lun","family":"Chen","sequence":"first","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leana","family":"Golubchik","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ranjan","family":"Pal","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"https:\/\/t.ly\/9bo0 Admissions Transparency Data New College of the Humanities London United Kingdom"},{"key":"e_1_3_3_3_2","unstructured":"https:\/\/t.ly\/qaaZ Admissions Transparency Implementation Working Group Department of Education Skills and Employment Australian Government"},{"key":"e_1_3_3_4_2","unstructured":"https:\/\/www.oecd.org\/gov\/open-government\/ Open Government"},{"key":"e_1_3_3_5_2","unstructured":"https:\/\/goo.gl\/UDoF64 U.S. Census Bureau Historical Income Tables: People"},{"key":"e_1_3_3_6_2","first-page":"PNAS\u20130904891106","article-title":"Predicting social security numbers from public data","author":"Acquisti Alessandro","year":"2009","unstructured":"Alessandro Acquisti and Ralph Gross. 2009. 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