The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Privacy preserving mining of association rules
Abstract
We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transactions. While it is feasible to recover association rules and preserve privacy using a straightforward "uniform" randomization, the discovered rules can unfortunately be exploited to find privacy breaches. We analyze the nature of privacy breaches and propose a class of randomization operators that are much more effective than uniform randomization in limiting the breaches. We derive formulae for an unbiased support estimator and its variance, which allow us to recover itemset supports from randomized datasets, and show how to incorporate these formulae into mining algorithms. Finally, we present experimental results that validate the algorithm by applying it on real datasets.
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Self-citationClassification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Cited by
L -diversity: Privacy beyond k -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617
Self-citationPrivacy-preserving data publishing: A survey of recent developments
Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704
Privacy-preserving k -means clustering over vertically partitioned data
Vaidya, J., & Clifton, C. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956776 · Cited by 626
A General Survey of Privacy-Preserving Data Mining Models and Algorithms
Aggarwal, C. C., & Yu, P. S. (2008) · Advances in Database Systems · doi:10.1007/978-0-387-70992-5_2 · Cited by 469
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
Du, W., Han, Y. S., & Chen, S. (2004) · Proceedings of the 2004 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.21 · Cited by 366
Metadata
- Authors
- Evfimievski, A., Srikant, R., Agrawal, R., & Gehrke, J.
- Venue
- Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2002-07-23
- Pages
- 217-228
- OpenAlex
- W2078937669
- Cited by
- 768
- References
- 3
- FWCI
- 35.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Evfimievski, A., Srikant, R., Agrawal, R., & Gehrke, J. (2002). Privacy preserving mining of association rules. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/775047.775080
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