An Attacker's View of Distance Preserving Maps for Privacy Preserving Data Mining

@inproceedings{Liu2006AnAV,
  title={An Attacker's View of Distance Preserving Maps for Privacy Preserving Data Mining},
  author={Kun Liu and Chris Giannella and Hillol Kargupta},
  booktitle={PKDD},
  year={2006}
}
We examine the effectiveness of distance preserving transformations in privacy preserving data mining. These techniques are potentially very useful in that some important data mining algorithms can be efficiently applied to the transformed data and produce exactly the same results as if applied to the original data e.g. distance-based clustering, k-nearest neighbor classification. However, the issue of how well the original data is hidden has, to our knowledge, not been carefully studied. We… CONTINUE READING
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