(Enter summary)
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... (Update)
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BibTeX entry: (Update)
A. Evfimievski, R. Srikant, R. Agrawal and J. Gehrke, "Privacy Preserving Mining of Association Rules", Proc. of 8th ACM $IGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD), July 2002. http://citeseer.ist.psu.edu/article/evfimievski02privacy.html More
@misc{ evfimievski02privacy,
author = "A. Evfimievski and R. Srikant and R. Agrawal and J. Gehrke",
title = "Privacy Preserving Mining of Association Rules",
text = "A. Evfimievski, R. Srikant, R. Agrawal and J. Gehrke, Privacy Preserving
Mining of Association Rules, Proc. of 8th ACM $IGKDD Intl. Conf. on Knowledge
Discovery and Data Mining (KDD), July 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/article/evfimievski02privacy.html" }
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