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 generic PDF 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.
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Tan, V.Y.F., Ng, SK. (2007). Generic Probability Density Function Reconstruction for Randomization in Privacy-Preserving Data Mining. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_7
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DOI: https://doi.org/10.1007/978-3-540-73499-4_7
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