Corpus ID: 233289863

Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning

@article{Rauch2021AchievingDP,
  title={Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning},
  author={Jens Rauch and Iyiola E. Olatunji and Megha Khosla},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.07938}
}
When applying outlier detection in settings where data is sensitive, mechanisms which guarantee the privacy of the underlying data are needed. The k-nearest neighbors (k-NN) algorithm is a simple and one of the most effective methods for outlier detection. So far, there have been no attempts made to develop a differentially private ( -DP) approach for k-NN based outlier detection. Existing approaches often relax the notion of -DP and employ other methods than k-NN. We propose a method for k-NN… Expand

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References

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