On the Privacy Preserving Properties of Random Data Perturbation Techniques

  • Authors:
  • Hillol Kargupta;Souptik Datta;Qi Wang;Krishnamoorthy Sivakumar

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
  • Year:
  • 2003

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Abstract

Privacy is becoming an increasingly important issue inmany data mining applications. This has triggered the developmentof many privacy-preserving data mining techniques.A large fraction of them use randomized data distortiontechniques to mask the data for preserving the privacyof sensitive data. This methodology attempts to hidethe sensitive data by randomly modifying the data values oftenusing additive noise. This paper questions the utility ofthe random value distortion technique in privacy preservation.The paper notes that random objects (particularly randommatrices) have "predictable" structures in the spectraldomain and it develops a random matrix-based spectral filteringtechnique to retrieve original data from the datasetdistorted by adding random values. The paper presents thetheoretical foundation of this filtering method and extensiveexperimental results to demonstrate that in many cases randomdata distortion preserve very little data privacy. Thepaper also points out possible avenues for the developmentof new privacy-preserving data mining techniques like exploitingmultiplicative and colored noise for preserving privacyin data mining applications.