000199247 001__ 199247
000199247 005__ 20180913062522.0
000199247 0247_ $$2doi$$a10.1109/Icdm.2013.57
000199247 022__ $$a1550-4786
000199247 02470 $$2ISI$$a000332874200025
000199247 037__ $$aCONF
000199247 245__ $$aPermutation-based Sequential Pattern Hiding
000199247 260__ $$bIeee$$c2013$$aNew York
000199247 269__ $$a2013
000199247 300__ $$a10
000199247 336__ $$aConference Papers
000199247 490__ $$aIEEE International Conference on Data Mining
000199247 520__ $$aSequence data are increasingly shared to enable mining applications, in various domains such as marketing, telecommunications, and healthcare. This, however, may expose sensitive sequential patterns, which lead to intrusive inferences about individuals or leak confidential information about organizations. This paper presents the first permutation-based approach to prevent this threat. Our approach hides sensitive patterns by replacing them with carefully selected permutations that avoid changes in the set of frequent nonsensitive patterns (side-effects) and in the ordering information of sequences (distortion). By doing so, it retains data utility in sequence mining and tasks based on itemset properties, as permutation preserves the support of items, unlike deletion, which is used in existing works. To realize our approach, we develop an efficient and effective algorithm for generating permutations with minimal side-effects and distortion. This algorithm also avoids implausible symbol orderings that may exist in certain applications. In addition, we propose a method to hide sensitive patterns from a sequence dataset. Extensive experiments verify that our method allows significantly more accurate data analysis than the state-of-the-art approach.
000199247 700__ $$0246101$$g221256$$aGwadera, Robert
000199247 700__ $$aGkoulalas-Divanis, Aris
000199247 700__ $$aLoukides, Grigorios
000199247 7112_ $$aIEEE 13th International Conference on Data Mining (ICDM)
000199247 720_1 $$aXiong, H.$$eed.
000199247 720_1 $$aKarypis, G.$$eed.
000199247 720_1 $$aThuraisingham, B.$$eed.
000199247 720_1 $$aCook, D.$$eed.
000199247 720_1 $$aWu, X.$$eed.
000199247 773__ $$t2013 Ieee 13Th International Conference On Data Mining (Icdm)$$q241-250
000199247 909C0 $$xU10405$$0252004$$pLSIR
000199247 909CO $$pconf$$pIC$$ooai:infoscience.tind.io:199247
000199247 917Z8 $$x134136
000199247 937__ $$aEPFL-CONF-199247
000199247 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000199247 980__ $$aCONF