A new perspective of privacy protection : Unique distinct l-SR diversity

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DOIResolve DOI: http://doi.org/10.1109/PST.2010.5593253
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ConferenceEighth Annual Conference on Privacy, Security and Trust (PST 2010), August 17-19, 2010, Ottawa, Ontario
Pages110117; # of pages: 8
AbstractMore and more public data sets which contain information about individuals are published in recent years. The urgency to reduce the risk of the privacy disclosure from such data sets makes the approaches of privacy protection for data publishing be widely employed. There are two popular models for privacy protection: k-anonymity and l-diversity. kanonymity focuses on reducing the probability of identifying a particular person, which requires that each equivalence class (a set of records with same identifier attributes) contains at least k records. l-diversity concentrates on reducing the inference from released sensitive attributes. It requires that each equivalence class has at least l “well-represented” sensitive attribute values. In this study, we view the privacy protection problem in a brand new perspective. We proposed a new model, Unique Distinct l- SR diversity based on the sensitivity of private information. Also, we presented two performance measures for how much sensitive information can be inferred from an equivalence class. l-SR diversity algorithm was implemented to achieve Unique Distinct l-SR diversity. We tested l-SR diversity on one benchmark data set and three synthetic data sets, and compared it with other l-diversity algorithms. The results show that our algorithm achieved better performance on minimizing inference of sensitive information and reached the comparable generalization data quality compared with other data publishing algorithms.
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AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number16067301
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Record identifiercad88c75-4cb8-412f-b691-e256bd75d28f
Record created2010-11-03
Record modified2016-05-09
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