Generic Probability Density Function Reconstruction for Randomization in Privacy-Preserving Data Mining

Authors:
Vincent Yan Tan;See-Kiong Ng
Affiliations:
Massachusetts Institute of Technology (MIT), Cambridge, MA 02139,;Institute for Infocomm Research (I2R), 119613, Singapore
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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Abstract

Data perturbation with random noise signals has been shown to be useful for data hiding in privacy-preserving data mining. Perturbation methods based on additive randomization allows accurate estimation of the Probability Density Function (PDF) via the Expectation-Maximization (EM) algorithm but it has been shown that noise-filtering techniques can be used to reconstruct the original data in many cases, leading to security breaches. In this paper, we propose a genericPDF reconstruction algorithm that can be used on non-additive (and additive) randomization techiques for the purpose of privacy-preserving data mining. This two-step reconstruction algorithm is based on Parzen-Window reconstruction and Quadratic Programming over a convex set --- the probability simplex. Our algorithm eliminates the usual need for the iterative EM algorithm and it is generic for most randomization models. The simplicity of our two-step reconstruction algorithm, without iteration, also makes it attractive for use when dealing with large datasets.