Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.

[1]  George V. Moustakides,et al.  A Max-Min Approach for Hiding Frequent Itemsets , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[2]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[3]  Mark S. Ackerman,et al.  Beyond Concern: Understanding Net Users' Attitudes About Online Privacy , 1999, ArXiv.

[4]  Jayant R. Haritsa,et al.  Mining Association Rules under Privacy Constraints , 2008, Privacy-Preserving Data Mining.

[5]  Jayant R. Haritsa,et al.  Maintaining Data Privacy in Association Rule Mining , 2002, VLDB.

[6]  Sumit Sarkar,et al.  Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns , 2005, Inf. Syst. Res..

[7]  Minghua Chen,et al.  Optimal Random Perturbation at Multiple Privacy Levels , 2009, Proc. VLDB Endow..

[8]  Yannis Theodoridis,et al.  A quantitative and qualitative ANALYSIS of blocking in association rule hiding , 2004, WPES '04.

[9]  A. Tamhane.  Randomized Response Techniques for Multiple Sensitive Attributes , 1981 .

[10]  Vassilios S. Verykios,et al.  An Experimental Study of Distortion-Based Techniques for Association Rule Hiding , 2004, DBSec.

[11]  Aris Gkoulalas-Divanis,et al.  A Survey of Association Rule Hiding Methods for Privacy , 2008, Privacy-Preserving Data Mining.

[12]  A. Adam.  Whatever happened to information systems ethics? Caught between the devil and the deep blue sea , 2004 .

[13]  Aris Gkoulalas-Divanis,et al.  An integer programming approach for frequent itemset hiding , 2006, CIKM '06.

[14]  Yufei Tao,et al.  Personalized privacy preservation , 2006, Privacy-Preserving Data Mining.

[15]  Chris Clifton,et al.  Privacy-preserving distributed mining of association rules on horizontally partitioned data , 2004, IEEE Transactions on Knowledge and Data Engineering.

[16]  S L Warner,et al.  Randomized response: a survey technique for eliminating evasive answer bias. , 1965, Journal of the American Statistical Association.

[17]  Wei Zhao,et al.  A New Scheme on Privacy Preserving Association Rule Mining , 2004, PKDD.

[18]  Arbee L. P. Chen,et al.  Hiding Sensitive Association Rules with Limited Side Effects , 2007, IEEE Transactions on Knowledge and Data Engineering.

[19]  Philip S. Yu,et al.  A border-based approach for hiding sensitive frequent itemsets , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[20]  Gu Si-yang,et al.  Privacy preserving association rule mining in vertically partitioned data , 2006 .

[21]  Ron Kohavi,et al.  Real world performance of association rule algorithms , 2001, KDD '01.

[22]  Shyue-Liang Wang,et al.  Using unknowns for hiding sensitive predictive association rules , 2005, IRI -2005 IEEE International Conference on Information Reuse and Integration, Conf, 2005..