{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:07:19Z","timestamp":1758272839441},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"1-2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2010,9]]},"abstract":"<jats:p>\n            The publication of transaction data, such as market basket data, medical records, and query logs, serves the public benefit. Mining such data allows for the derivation of association rules that connect certain items to others with measurable confidence. Still, this type of data analysis poses a privacy threat; an adversary having partial information on a person's behavior may\n            <jats:italic>confidently<\/jats:italic>\n            associate that person to an item deemed to be\n            <jats:italic>sensitive<\/jats:italic>\n            . Ideally, an\n            <jats:italic>anonymization<\/jats:italic>\n            of such data should lead to an\n            <jats:italic>inference-proof<\/jats:italic>\n            version that prevents the association of individuals to sensitive items, while otherwise allowing for truthful associations to be derived. Original approaches to this problem were based on value\n            <jats:italic>perturbation<\/jats:italic>\n            , damaging data integrity. Recently, value\n            <jats:italic>generalization<\/jats:italic>\n            has been proposed as an alternative; still, approaches based on it have assumed\n            <jats:italic>either<\/jats:italic>\n            that all items are equally sensitive,\n            <jats:italic>or<\/jats:italic>\n            that some are sensitive and can be known to an adversary\n            <jats:italic>only<\/jats:italic>\n            by association, while others are non-sensitive and can be known directly. Yet in reality there\n            <jats:italic>is<\/jats:italic>\n            a distinction between sensitive and non-sensitive items, but an adversary may possess information on\n            <jats:italic>any<\/jats:italic>\n            of them. Most critically, no antecedent method aims at a clear\n            <jats:italic>inference-proof<\/jats:italic>\n            privacy guarantee. In this paper, we propose \u03c1-uncertainty, the\n            <jats:italic>first<\/jats:italic>\n            , to our knowledge, privacy concept that\n            <jats:italic>inherently<\/jats:italic>\n            safeguards against sensitive associations\n            <jats:italic>without<\/jats:italic>\n            constraining the nature of an adversary's knowledge and\n            <jats:italic>without<\/jats:italic>\n            falsifying data. The problem of achieving \u03c1-uncertainty with low information loss is challenging because it is\n            <jats:italic>natural<\/jats:italic>\n            . A trivial solution is to suppress all sensitive items. We develop more sophisticated schemes. In a broad experimental study, we show that the problem is solved non-trivially by a technique that combines generalization and suppression, which also achieves favorable results compared to a baseline perturbation-based scheme.\n          <\/jats:p>","DOI":"10.14778\/1920841.1920971","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"1033-1044","source":"Crossref","is-referenced-by-count":51,"title":["\u03c1-uncertainty"],"prefix":"10.14778","volume":"3","author":[{"given":"Jianneng","family":"Cao","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panagiotis","family":"Karras","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chedy","family":"Ra\u00efssi","sequence":"additional","affiliation":[{"name":"INRIA, Nancy Grand-Est, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kian-Lee","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2010,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242595"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"key":"e_1_2_1_3_1","volume-title":"VLDB","author":"Agrawal R.","year":"1994","unstructured":"R. Agrawal and R. Srikant . Fast algorithms for mining association rules in large databases . In VLDB , 1994 . R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. 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Savasere , E. Omiecinski , and S. B. Navathe . An efficient algorithm for mining association rules in large databases . In VLDB , 1995 . A. Savasere, E. Omiecinski, and S. B. Navathe. An efficient algorithm for mining association rules in large databases. In VLDB, 1995."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/604264.604271"},{"key":"e_1_2_1_22_1","volume-title":"VLDB","author":"Srikant R.","year":"1995","unstructured":"R. Srikant and R. Agrawal . Mining generalized association rules . In VLDB , 1995 . R. Srikant and R. Agrawal. Mining generalized association rules. In VLDB, 1995."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1453856.1453874"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2004.1269668"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2009.184"},{"key":"e_1_2_1_26_1","volume-title":"VLDB","author":"Wong R. C.-W.","year":"2007","unstructured":"R. C.-W. Wong , A. W.-C. Fu , K. Wang , and J. Pei . Minimality attack in privacy preserving data publishing . In VLDB , 2007 . R. C.-W. Wong, A. W.-C. Fu, K. Wang, and J. Pei. Minimality attack in privacy preserving data publishing. In VLDB, 2007."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.11"},{"key":"e_1_2_1_28_1","volume-title":"VLDB","author":"Xiao X.","year":"2006","unstructured":"X. Xiao and Y. Tao . Anatomy: simple and effective privacy preservation . In VLDB , 2006 . X. Xiao and Y. Tao. Anatomy: simple and effective privacy preservation. In VLDB, 2006."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142500"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401982"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014091"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.846291"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502572"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/1920841.1920971","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:44:12Z","timestamp":1672227852000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/1920841.1920971"}},"subtitle":["inference-proof transaction anonymization"],"short-title":[],"issued":{"date-parts":[[2010,9]]},"references-count":33,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2010,9]]}},"alternative-id":["10.14778\/1920841.1920971"],"URL":"https:\/\/doi.org\/10.14778\/1920841.1920971","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2010,9]]}}}