A Partial Optimization Approach for Privacy Preserving Frequent Itemset Mining
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Date
2010-01
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Citation of Original Publication
Mukherjee, Shibnath; Gangopadhyay, Aryya; Chen, Zhiyuan; A Partial Optimization Approach for Privacy Preserving Frequent Itemset Mining; International Journal of Computational Models and Algorithms in Medicine (IJCMAM) 1(1), 19-33, January 2010; https://doi.org/10.4018/jcmam.2010072002
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Abstract
While data mining has been widely acclaimed as a technology that can bring potential benefits to organizations, such efforts may be negatively impacted by the possibility of discovering sensitive patterns, particularly in patient data. In this article the authors present an approach to identify the optimal set of transactions that, if sanitized, would result in hiding sensitive patterns while reducing the accidental hiding of legitimate patterns and the damage done to the database as much as possible. Their methodology allows the user to adjust their preference on the weights assigned to benefits in terms of the number of restrictive patterns hidden, cost in terms of the number of legitimate patterns hidden, and damage to the database in terms of the difference between marginal frequencies of items for the original and sanitized databases. Most approaches in solving the given problem found in literature are all-heuristic based without formal treatment for optimality. While in a few work, ILP has been used previously as a formal optimization approach, the novelty of this method is the extremely low cost-complexity model in contrast to the others. They implement our methodology in C and C++ and ran several experiments with synthetic data generated with the IBM synthetic data generator. The experiments show excellent results when compared to those in the literature.