Data privacy
Rakesh Agrawal
PKDD 2004
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.
Rakesh Agrawal
PKDD 2004
Rakesh Agrawal, Jerry Kiernan, et al.
ICDE 2003
Alexandre Evfimievski, Johannes Gehrke, et al.
SIGMOD/PODS/ 2003
Rakesh Agrawal, Ramakrishnan Srikant, et al.
SIGMOD 2005