Sunita Sarawagi, Shiby Thomas, et al.
SIGMOD Record
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. © 2003 Published by Elsevier Ltd.
Sunita Sarawagi, Shiby Thomas, et al.
SIGMOD Record
Kamal Ali, Stefanos Manganaris, et al.
KDD 1997
Rakesh Agrawal, Ramakrishnan Srikant
WWW 2002
Ramakrishnan Srikant, Quoc Vu, et al.
KDD 1997