Discovering top-k patterns with differential privacy–an accurate approach

Abstract

Frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data (e.g., user behavior records, electronic health records), directly releasing the discovered frequent patterns with support counts will carry significant risk to the privacy of individuals. In this paper, we study the problem of how to accurately find the top-k frequent patterns with noisy support counts on transactional databases while satisfying differential privacy. We propose an algorithm, called differentially private frequent pattern (DFP-Growth), that integrates a Laplace mechanism and an exponential mechanism to avoid privacy leakage. We theoretically prove that the proposed method is (λ, δ)-useful and differentially private. To boost the accuracy of the returned noisy support counts, we take consistency constraints into account to conduct constrained inference in the post-processing step. Extensive experiments, using several real datasets, confirm that our algorithm generates highly accurate noisy support counts and top-k frequent patterns.

Keywords

frequent pattern mining / differential privacy / constrained inference

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg