{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:08:29Z","timestamp":1762430909692},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2009,8]]},"abstract":"<jats:p>Privacy in data publishing has received much attention recently. The key to defining privacy is to model knowledge of the attacker -- if the attacker is assumed to know too little, the published data can be easily attacked, if the attacker is assumed to know too much, the published data has little utility. Previous work considered either quite ignorant adversaries or nearly omniscient adversaries.<\/jats:p>\n          <jats:p>\n            In this paper, we introduce a new class of adversaries that we call\n            <jats:italic>realistic adversaries<\/jats:italic>\n            who live in the unexplored space in between. Realistic adversaries have knowledge from external sources with an associated stubbornness indicating the strength of their knowledge. We then introduce a novel privacy framework called epsilon-privacy that allows us to guard against realistic adversaries. We also show that prior privacy definitions are instantiations of our framework. In a thorough experimental study with real census data we show that e-privacy allows us to publish data with high utility while defending against strong adversaries.\n          <\/jats:p>","DOI":"10.14778\/1687627.1687717","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"790-801","source":"Crossref","is-referenced-by-count":27,"title":["Data publishing against realistic adversaries"],"prefix":"10.14778","volume":"2","author":[{"given":"Ashwin","family":"Machanavajjhala","sequence":"first","affiliation":[{"name":"Yahoo! Research, Santa Clara, CA"}]},{"given":"Johannes","family":"Gehrke","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}]},{"given":"Michaela","family":"G\u00f6tz","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}]}],"member":"320","published-online":{"date-parts":[[2009,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066187"},{"key":"e_1_2_1_2_1","volume-title":"National Conference on Artificial Intelligence AAAI","author":"Bacchus F.","year":"1992","unstructured":"F. Bacchus , A. J. Grove , J. Y. Halpern , and D. Koller . From statistics to beliefs . In National Conference on Artificial Intelligence AAAI , 1992 . F. Bacchus, A. J. Grove, J. Y. Halpern, and D. Koller. From statistics to beliefs. In National Conference on Artificial Intelligence AAAI, 1992."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.42"},{"key":"e_1_2_1_4_1","author":"Blei D. 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Using priors to model realistic adversaries. Technical report , Cornell University , 2009 . A. Machanavajjhala, J. Gehrke, and M. Goetz. Using priors to model realistic adversaries. Technical report, Cornell University, 2009."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2006.1"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497436"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2007.367858"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007568.1007633"},{"key":"e_1_2_1_20_1","volume-title":"The Uncertain Reasoner's Companion","author":"Paris J. B.","year":"1994","unstructured":"J. B. Paris . The Uncertain Reasoner's Companion . Cambridge University Press , 1994 . J. B. Paris. The Uncertain Reasoner's Companion. Cambridge University Press, 1994."},{"key":"e_1_2_1_21_1","volume-title":"The boundary between privacy and utility in data publishing. 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