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2003
IEEE

On the Privacy Preserving Properties of Random Data Perturbation Techniques

9 years 1 months ago
On the Privacy Preserving Properties of Random Data Perturbation Techniques
Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacy-preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. This paper questions the utility of the random value distortion technique in privacy preservation. The paper notes that random objects (particularly random matrices) have “predictable” structures in the spectral domain and it develops a random matrix-based spectral filtering technique to retrieve original data from the dataset distorted by adding random values. The paper presents the theoretical foundation of this filtering method and extensive experimental results to demonstrate that in many cases random data distortion preserve very little data privacy.
Hillol Kargupta, Souptik Datta, Qi Wang, Krishnamo
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where ICDM
Authors Hillol Kargupta, Souptik Datta, Qi Wang, Krishnamoorthy Sivakumar
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