Privacy preserving mining of association rules

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Privacy preserving mining of association rules

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Edmonton, Alberta, Canada

SESSION: Frequent patterns II table of contents

Pages: 217 - 228

Year of Publication: 2002

ISBN:1-58113-567-X

Authors

Alexandre Evfimievski	IBM Almaden Research Center, San Jose, CA
Ramakrishnan Srikant	IBM Almaden Research Center, San Jose, CA
Rakesh Agrawal	IBM Almaden Research Center, San Jose, CA
Johannes Gehrke	IBM Almaden Research Center, San Jose, CA

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
: AAAI

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ACM New York, NY, USA

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Downloads (6 Weeks): 21, Downloads (12 Months): 111, Citation Count: 98

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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.

REFERENCES

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