{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T20:10:39Z","timestamp":1774642239039,"version":"3.50.1"},"reference-count":72,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2007,3,1]],"date-time":"2007-03-01T00:00:00Z","timestamp":1172707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2007,3]]},"abstract":"<jats:p>\n            Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called\n            <jats:italic>k<\/jats:italic>\n            -anonymity has gained popularity. In a\n            <jats:italic>k<\/jats:italic>\n            -anonymized dataset, each record is indistinguishable from at least\n            <jats:italic>k<\/jats:italic>\n            \u2212 1 other records with respect to certain identifying attributes.\n          <\/jats:p>\n          <jats:p>\n            In this article, we show using two simple attacks that a\n            <jats:italic>k<\/jats:italic>\n            -anonymized dataset has some subtle but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and we show that\n            <jats:italic>k<\/jats:italic>\n            -anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks, and we propose a novel and powerful privacy criterion called \u2113-diversity that can defend against such attacks. In addition to building a formal foundation for \u2113-diversity, we show in an experimental evaluation that \u2113-diversity is practical and can be implemented efficiently.\n          <\/jats:p>","DOI":"10.1145\/1217299.1217302","type":"journal-article","created":{"date-parts":[[2007,6,8]],"date-time":"2007-06-08T15:00:08Z","timestamp":1181314808000},"page":"3","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2251,"title":["<i>L<\/i>\n            -diversity"],"prefix":"10.1145","volume":"1","author":[{"given":"Ashwin","family":"Machanavajjhala","sequence":"first","affiliation":[{"name":"Cornell University, Ithaca, NY"}]},{"given":"Daniel","family":"Kifer","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}]},{"given":"Johannes","family":"Gehrke","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}]},{"given":"Muthuramakrishnan","family":"Venkitasubramaniam","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY"}]}],"member":"320","published-online":{"date-parts":[[2007,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/76894.76895"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT). 183--199","author":"Aggarwal C. C.","unstructured":"Aggarwal , C. C. and Yu , P. S . 2004. A condensation approach to privacy preserving data mining . In Proceedings of the International Conference on Extending Database Technology (EDBT). 183--199 . Aggarwal, C. C. and Yu, P. S. 2004. A condensation approach to privacy preserving data mining. In Proceedings of the International Conference on Extending Database Technology (EDBT). 183--199."},{"key":"e_1_2_1_3_1","unstructured":"Aggarwal G. Feder T. Kenthapadi K. Motwani R. Panigrahy R. Thomas D. and Zhu A. 2004. k-anonymity: Algorithms and hardness. Tech. rep. Stanford University.  Aggarwal G. Feder T. Kenthapadi K. Motwani R. Panigrahy R. Thomas D. and Zhu A. 2004. k-anonymity: Algorithms and hardness. Tech. rep. Stanford University."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/375551.375602"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB). 516--527","author":"Agrawal R.","unstructured":"Agrawal , R. , Bayardo , R. J. , Faloutsos , C. , Kiernan , J. , Rantzau , R. , and Srikant , R . 2004. Auditing compliance with a hippocratic database . In Proceedings of the International Conference on Very Large Databases (VLDB). 516--527 . Agrawal, R., Bayardo, R. J., Faloutsos, C., Kiernan, J., Rantzau, R., and Srikant, R. 2004. Auditing compliance with a hippocratic database. In Proceedings of the International Conference on Very Large Databases (VLDB). 516--527."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/872757.872771"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB). 143--154","author":"Agrawal R.","unstructured":"Agrawal , R. , Kiernan , J. , Srikant , R. , and Xu , Y . 2002. Hippocratic databases . In Proceedings of the International Conference on Very Large Databases (VLDB). 143--154 . Agrawal, R., Kiernan, J., Srikant, R., and Xu, Y. 2002. Hippocratic databases. In Proceedings of the International Conference on Very Large Databases (VLDB). 143--154."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB).","author":"Agrawal R.","unstructured":"Agrawal , R. and Srikant , R . 1994. Fast Algorithms for Mining Association Rules in Large Databases . 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