On the Privacy Preserving Properties of Random Data Perturbation Techniques

  title={On the Privacy Preserving Properties of Random Data Perturbation Techniques},
  author={Hillol Kargupta and Souptik Datta and Qi Wang and Krishnamoorthy Sivakumar},
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… CONTINUE READING
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