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a.ta.ks on a.onymiza.ion-Ba.ed Priva.y-Preserving: a.Survey for Da.a.Mining a.d Da.a.Publishing

Journa. of Informa.ion Security 2013年第2期4卷 101-112页

作者： a.ou-el-ela.a.dou Hussien Nermin Ha.za.hesham a. hefny Depa.tment of Computer a.d Informa.ion Sciences Institute of Statistical Studies and Research Cairo University Giza Egypt Depa.tment of Computer Science Faculty of Science and Humanities Shaqra University Shaqra KSA

Da.a.mining is the extra.tion of va.t interesting pa.terns or knowledge from huge a.ount of da.a. The initia. idea.of priva.y-preserving da.a.mining PPDM wa. to extend tra.itiona. da.a.mining techniques to work with the da.a.modified to ma.k sensitive informa.ion. The key issues were how to modify the da.a.a.d how to recover the da.a.mining result from the modified da.a. Priva.y-preserving da.a.mining considers the problem of running da.a.mining a.gorithms on confidentia. da.a.tha. is not supposed to be revea.ed even to the pa.ty running the a.gorithm. In contra.t, priva.y-preserving da.a.publishing (PPDP) ma. not necessa.ily be tied to a.specific da.a.mining ta.k, a.d the da.a.mining ta.k ma. be unknown a. the time of da.a.publishing. PPDP studies how to tra.sform ra. da.a.into a.version tha. is immunized a.a.nst priva.y a.ta.ks but tha. still supports effective da.a.mining ta.ks. Priva.y-preserving for both da.a.mining (PPDM) a.d da.a.publishing (PPDP) ha. become increa.ingly popula. beca.se it a.lows sha.ing of priva.y sensitive da.a.for a.a.ysis purposes. One well studied a.proa.h is the k-a.onymity model [1] which in turn led to other models such a. confidence bounding, l-diversity, t-closeness, (α,k)-a.onymity, etc. In pa.ticula., a.l known mecha.isms try to minimize informa.ion loss a.d such a. a.tempt provides a.loophole for a.ta.ks. The a.m of this pa.er is to present a.survey for most of the common a.ta.ks techniques for a.onymiza.ion-ba.ed PPDM & PPDP a.d expla.n their effects on Da.a.Priva.y.

关键词： Priva.y k-a.onymity Da.a Mining Priva.y-Preserving Da.a Publishing Priva.y-Preserving Da.a Mining

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Utility-Ba.ed a.onymiza.ion Using Genera.iza.ion Bounda.ies to Protect Sensitive a.tributes

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Journa. of Informa.ion Security 2015年第3期6卷 179-196页

作者： a.ou-el-ela.a.dou Hussien Na.y Ra.a.a. Da.wish hesham a. hefny Depa.tment of Computer Science Faculty of Science and Arts Shaqra University Shaqra KSA Depa.tment of Computer a.d Informa.ion Sciences Institute of Statistical Studies and Research Cairo University Cairo Egypt

Priva.y preserving da.a.mining (PPDM) ha. become more a.d more importa.t beca.se it a.lows sha.ing of priva.y sensitive da.a.for a.a.ytica. purposes. a.big number of priva.y techniques were developed most of which used the k-a.onymity property which ha.e ma.y shortcomings, so other priva.y techniques were introduced (l-diversity, p-sensitive k-a.onymity, (α, k)-a.onymity, t-closeness, etc.). While they a.e different in their methods a.d qua.ity of their results, they a.l focus first on ma.king the da.a. a.d then protecting the qua.ity of the da.a. This pa.er is concerned with providing a. enha.ced priva.y technique tha. combines some a.onymity techniques to ma.nta.n both priva.y a.d da.a.utility by considering the sensitivity va.ues of a.tributes in queries using sensitivity weights which determine ta.ing in a.count utility-ba.ed a.onymiza.ion a.d then only queries ha.ing sensitive a.tributes whose va.ues exceed threshold a.e to be cha.ged using genera.iza.ion bounda.ies. The threshold va.ue is ca.cula.ed depending on the different weights a.signed to individua. a.tributes which ta.e into a.count the utility of ea.h a.tribute a.d those pa.ticula. a.tributes whose tota. weights exceed the threshold va.ues is cha.ged using genera.iza.ion bounda.ies a.d the other queries ca. be directly published. Experiment results using UT da.la. a.onymiza.ion toolbox on rea. da.a.set a.ult da.a.a.e from the UC ma.hine lea.ning repository show tha. a.though the proposed technique preserves priva.y, it a.so ca. ma.nta.n the utility of the publishing da.a.

关键词： Priva.y Priva.y Preserving Da.a Mining K-a.onymity Genera.iza.ion Bounda.ies Suppression

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