Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning
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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 (ϵ-DP) approach for k-NN based outlier detection. Existing approaches often relax the notion of ϵ-DP and employ other methods than k-NN. We propose a method for k-NN based outlier detection by separating the procedure into a fitting step on reference inlier data and then apply the outlier classifier to new data. We achieve ϵ-DP for both the fitting algorithm and the outlier classifier with respect to the reference data by partitioning the dataset into a uniform grid, which yields low global sensitivity. Our approach yields nearly optimal performance on real-world data with varying dimensions when compared to the non-private versions of k-NN.
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