{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:33:24Z","timestamp":1750307604402,"version":"3.41.0"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2010,4,1]],"date-time":"2010-04-01T00:00:00Z","timestamp":1270080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001475","name":"Nanyang Technological University","doi-asserted-by":"publisher","award":["M58020016AcRF Tier 1"],"award-info":[{"award-number":["M58020016AcRF Tier 1"]}],"id":[{"id":"10.13039\/501100001475","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001641","name":"Glaucoma Research Foundation","doi-asserted-by":"publisher","award":["4169\/094173\/084161\/07"],"award-info":[{"award-number":["4169\/094173\/084161\/07"]}],"id":[{"id":"10.13039\/100001641","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Database Syst."],"published-print":{"date-parts":[[2010,4]]},"abstract":"<jats:p>\n            Numerous generalization techniques have been proposed for privacy-preserving data publishing. Most existing techniques, however, implicitly assume that the adversary knows little about the anonymization algorithm adopted by the data publisher. Consequently, they cannot guard against privacy attacks that exploit various characteristics of the anonymization mechanism. This article provides a practical solution tothis problem. First, we propose an analytical model for evaluating disclosure risks, when an adversary knows\n            <jats:italic>everything<\/jats:italic>\n            in the anonymization process, except the sensitive values. Based on this model, we develop a privacy principle,\n            <jats:italic>transparent l-diversity<\/jats:italic>\n            , which ensures privacy protection against such powerful adversaries. We identify three algorithms that achieve transparent\n            <jats:italic>l<\/jats:italic>\n            -diversity, and verify their effectiveness and efficiency through extensive experiments with real data.\n          <\/jats:p>","DOI":"10.1145\/1735886.1735887","type":"journal-article","created":{"date-parts":[[2010,5,4]],"date-time":"2010-05-04T14:14:06Z","timestamp":1272982446000},"page":"1-48","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Transparent anonymization"],"prefix":"10.1145","volume":"35","author":[{"given":"Xiaokui","family":"Xiao","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Tao","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Shatin, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nick","family":"Koudas","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2010,5,3]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150460"},{"key":"e_1_2_2_2_1","volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB'05)","author":"Aggarwal C. C.","year":"2005","unstructured":"Aggarwal , C. C. 2005 . On k-anonymity and the curse of dimensionality . In Proceedings of the International Conference on Very Large Databases (VLDB'05) . 901--909. Aggarwal, C. C. 2005. On k-anonymity and the curse of dimensionality. In Proceedings of the International Conference on Very Large Databases (VLDB'05). 901--909."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30570-5_17"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066187"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(96)00003-3"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.42"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/11844662_4"},{"key":"e_1_2_2_8_1","volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB'07)","author":"Chen B.-C.","year":"2007","unstructured":"Chen , B.-C. , Ramakrishnan , R. , and LeFevre , K. 2007 . Privacy skyline: Privacy with multidimensional adversarial knowledge . In Proceedings of the International Conference on Very Large Databases (VLDB'07) . 770--781. Chen, B.-C., Ramakrishnan, R., and LeFevre, K. 2007. Privacy skyline: Privacy with multidimensional adversarial knowledge. In Proceedings of the International Conference on Very Large Databases (VLDB'07). 770--781."},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/773153.773174"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/355744.355745"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.143"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1114244.1114246"},{"volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB'07)","author":"Ghinita G.","key":"e_1_2_2_14_1","unstructured":"Ghinita , G. , Karras , P. , Kalnis , P. , and Mamoulis , N . 2007. Fast data anonymization with low information loss . In Proceedings of the International Conference on Very Large Databases (VLDB'07) . 758--769. Ghinita, G., Karras, P., Kalnis, P., and Mamoulis, N. 2007. Fast data anonymization with low information loss. In Proceedings of the International Conference on Very Large Databases (VLDB'07). 758--769."},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775089"},{"key":"e_1_2_2_16_1","unstructured":"Kendall M. and Stuart A. 1979. The Advanced Theory of Statistics 4th Ed. MacMillan New York.  Kendall M. and Stuart A. 1979. The Advanced Theory of Statistics 4th Ed. MacMillan New York."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559861"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142499"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066164"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2006.101"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150435"},{"volume-title":"Proceedings of the International Conference on Data Engineering (ICDE'07)","author":"Li N.","key":"e_1_2_2_22_1","unstructured":"Li , N. , Li , T. , and Venkatasubramanian , S . 2007. t-Closeness: Privacy beyond k-anonymity and l-diversity . In Proceedings of the International Conference on Data Engineering (ICDE'07) . 106--115. Li, N., Li, T., and Venkatasubramanian, S. 2007. t-Closeness: Privacy beyond k-anonymity and l-diversity. In Proceedings of the International Conference on Data Engineering (ICDE'07). 106--115."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217302"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497436"},{"volume-title":"Proceedings of the International Conference on Data Engineering (ICDE'07)","author":"Martin D. J.","key":"e_1_2_2_25_1","unstructured":"Martin , D. J. , Kifer , D. , Machanavajjhala , A. , Gehrke , J. , and Halpern , J. Y . 2007. Worst-Case background knowledge for privacy-preserving data publishing . In Proceedings of the International Conference on Data Engineering (ICDE'07) . 126--135. Martin, D. J., Kifer, D., Machanavajjhala, A., Gehrke, J., and Halpern, J. Y. 2007. Worst-Case background knowledge for privacy-preserving data publishing. 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In Proceedings of the IEEE International Conference on Data Mining (ICDM'04). 249--256."},{"volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB'07)","author":"Wong R. C.-W.","key":"e_1_2_2_36_1","unstructured":"Wong , R. C.-W. , Fu , A. W.-C. , Wang , K. , and Pei , J . 2007. Minimality attack in privacy preserving data publishing . In Proceedings of the International Conference on Very Large Databases (VLDB'07) . 543--554. Wong, R. C.-W., Fu, A. W.-C., Wang, K., and Pei, J. 2007. Minimality attack in privacy preserving data publishing. In Proceedings of the International Conference on Very Large Databases (VLDB'07). 543--554."},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150499"},{"volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB'06)","author":"Xiao X.","key":"e_1_2_2_38_1","unstructured":"Xiao , X. and Tao , Y . 2006a. Anatomy: Simple and effective privacy preservation . 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