{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T05:58:03Z","timestamp":1760853483061,"version":"3.41.0"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2010,7,1]],"date-time":"2010-07-01T00:00:00Z","timestamp":1277942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee, Hong Kong","doi-asserted-by":"publisher","award":["HKBU FRG\/08-09\/II-48FRG2\/09-10\/047FRG\/07-08\/II-23GRF 210808HKBU1\/05C"],"award-info":[{"award-number":["HKBU FRG\/08-09\/II-48FRG2\/09-10\/047FRG\/07-08\/II-23GRF 210808HKBU1\/05C"]}],"id":[{"id":"10.13039\/501100002920","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":[[2010,7]]},"abstract":"<jats:p>\n            This article examines a new problem of\n            <jats:italic>k<\/jats:italic>\n            -anonymity with respect to a reference dataset in privacy-aware location data publishing: given a user dataset and a sensitive event dataset, we want to generalize the user dataset such that by joining it with the event dataset through location, each event is covered by at least\n            <jats:italic>k<\/jats:italic>\n            users. Existing\n            <jats:italic>k<\/jats:italic>\n            -anonymity algorithms generalize every\n            <jats:italic>k<\/jats:italic>\n            user locations to the same vague value, regardless of the events. Therefore, they tend to overprotect against the privacy compromise and make the published data less useful. In this article, we propose a new generalization paradigm called\n            <jats:italic>local enlargement<\/jats:italic>\n            , as opposed to conventional hierarchy- or partition-based generalization. Local enlargement guarantees that user locations are enlarged just enough to cover all events\n            <jats:italic>k<\/jats:italic>\n            times, and thus maximize the usefulness of the published data. We develop an\n            <jats:italic>O<\/jats:italic>\n            (\n            <jats:italic>H<\/jats:italic>\n            <jats:sub>\n              <jats:italic>n<\/jats:italic>\n            <\/jats:sub>\n            )-approximate algorithm under the local enlargement paradigm, where\n            <jats:italic>n<\/jats:italic>\n            is the maximum number of events a user could possibly cover and\n            <jats:italic>H<\/jats:italic>\n            <jats:sub>\n              <jats:italic>n<\/jats:italic>\n            <\/jats:sub>\n            is the Harmonic number of\n            <jats:italic>n<\/jats:italic>\n            . With strong pruning techniques and mathematical analysis, we show that it runs efficiently and that the generalized user locations are up to several orders of magnitude smaller than those by the existing algorithms. In addition, it is robust enough to protect against various privacy attacks.\n          <\/jats:p>","DOI":"10.1145\/1806907.1806910","type":"journal-article","created":{"date-parts":[[2010,8,2]],"date-time":"2010-08-02T13:15:22Z","timestamp":1280754922000},"page":"1-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Privacy-aware location data publishing"],"prefix":"10.1145","volume":"35","author":[{"given":"Haibo","family":"Hu","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University, Kowloon, Hong Kong"}]},{"given":"Jianliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Kowloon, Hong Kong"}]},{"given":"Sai Tung","family":"On","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Kowloon, Hong Kong"}]},{"given":"Jing","family":"Du","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Kowloon, Hong Kong"}]},{"given":"Joseph Kee-Yin","family":"Ng","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Kowloon, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2010,7,30]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1109\/ICDE.2005.42"},{"doi-asserted-by":"publisher","key":"e_1_2_1_2_1","DOI":"10.1023\/A:1015231126594"},{"volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB'07)","author":"Chen B.-C.","key":"e_1_2_1_3_1"},{"volume-title":"Proceedings of the International Conference on Data Engineering (ICDE'07)","author":"Du Y.","key":"e_1_2_1_4_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_5_1","DOI":"10.1109\/ICDE.2005.143"},{"doi-asserted-by":"publisher","key":"e_1_2_1_6_1","DOI":"10.1109\/ICDCS.2005.48"},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.1109\/TMC.2007.1062"},{"volume-title":"Proceedings of the International Symposium in Spatial and Temporal Databases (SSTD'07)","author":"Ghinita G.","key":"e_1_2_1_8_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_9_1","DOI":"10.1145\/1242572.1242623"},{"doi-asserted-by":"publisher","key":"e_1_2_1_10_1","DOI":"10.1145\/1066116.1189037"},{"volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB'07)","author":"Iwuchukwu T.","key":"e_1_2_1_11_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_12_1","DOI":"10.1145\/775047.775089"},{"doi-asserted-by":"publisher","key":"e_1_2_1_13_1","DOI":"10.1145\/1066157.1066164"},{"doi-asserted-by":"publisher","key":"e_1_2_1_14_1","DOI":"10.1109\/ICDE.2006.101"},{"volume-title":"Proceedings of the 23rd IEEE International Conference on Data Engineering (ICDE'07)","author":"Li N.","key":"e_1_2_1_15_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_16_1","DOI":"10.1109\/ICDE.2006.1"},{"volume-title":"Proceedings of the International Conference on Data Engineering (ICDE'07)","author":"Martin D. J.","key":"e_1_2_1_17_1"},{"volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB'06)","author":"Mokbel M. F.","key":"e_1_2_1_18_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_19_1","DOI":"10.1109\/69.971193"},{"doi-asserted-by":"publisher","key":"e_1_2_1_20_1","DOI":"10.1142\/S021848850200165X"},{"unstructured":"Vazirani V. V. 2001. Approximation Algorithms. Springer.   Vazirani V. V. 2001. Approximation Algorithms. 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