论文信息 - Mining association rules with non-uniform privacy concerns

Mining association rules with non-uniform privacy concerns

Privacy concerns have become an important issue in data mining. A popular way to preserve privacy is to randomize the dataset to be mined in a systematic way and mine the randomized dataset instead. On the other hand, people usually have different privacy concerns for different attributes in data. E.g., in survey data, the sensitivity of questions varies. Appropriate use of this information can lead to more accurate data mining results. However, this information has not been fully utilized by many privacy preserving association rule mining algorithms.In this paper, we generalize the privacy preserving association rule mining problem by allowing different attributes to have different levels of privacy, that is, using different randomization factors for values of different attributes in the randomization process. We also propose an efficient algorithm RE (Recursive Estimation) to estimate the support of itemsets under this framework. Both theoretical and empirical results show that the use of non-uniform randomization factors improves the accuracy of the support estimates, compared to the use of one conservative randomization factor.

[1] B. E. Eckbo,et al. Appendix , 1826, Epilepsy Research.

[2] A Sufficient Condition for the Positive Definiteness of the Covariance Matrix of a Multivariate GARCH Model , 2001 .

[3] Jayant R. Haritsa,et al. On Addressing Efficiency Concerns in Privacy-Preserving Mining , 2003, DASFAA.

[4] Wenliang Du,et al. Privacy-preserving collaborative filtering using randomized perturbation techniques , 2003, Third IEEE International Conference on Data Mining.

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

[6] Ramakrishnan Srikant,et al. Privacy-preserving data mining , 2000, SIGMOD '00.

[7] Ramakrishnan Srikant,et al. Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[8] Alexandre V. Evfimievski,et al. Privacy preserving mining of association rules , 2002, Inf. Syst..

[9] Alexandre V. Evfimievski,et al. Limiting privacy breaches in privacy preserving data mining , 2003, PODS.

[10] Ramakrishnan Srikant,et al. Fast algorithms for mining association rules , 1998, VLDB 1998.

[11] Wenliang Du,et al. Using randomized response techniques for privacy-preserving data mining , 2003, KDD '03.

[12] Jaideep Vaidya,et al. Privacy preserving association rule mining in vertically partitioned data , 2002, KDD.