On the privacy preserving properties of random data perturbation techniques

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On the privacy preserving properties of random data perturbation techniques (2003)

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by Hillol Kargupta , Souptik Datta

Venue:	In ICDM
Citations:	190 - 6 self

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@INPROCEEDINGS{Kargupta03onthe,
    author = {Hillol Kargupta and Souptik Datta},
    title = {On the privacy preserving properties of random data perturbation techniques},
    booktitle = {In ICDM},
    year = {2003},
    pages = {99--106}
}

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Abstract

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. 1.

Keyphrases

random data perturbation technique sensitive data theoretical foundation filtering method data value many case many privacy-preserving data mining technique paper question original data large fraction data distortion technique random value distortion technique little data privacy random value random object privacy preservation random matrix-based spectral filtering technique extensive experimental result additive noise important issue data distortion predictable structure spectral domain many data mining application

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