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A privacy preserving technique for distance-based classification with worst case privacy guarantees
2008
Data & Knowledge Engineering
There has been relatively little work on privacy preserving techniques for distance based mining. The most widely used ones are additive perturbation methods and orthogonal transform based methods. These methods concentrate on privacy protection in the average case and provide no worst case privacy guarantee. However, the lack of privacy guarantee makes it difficult to use these techniques in practice, and causes possible privacy breach under certain attacking methods. This paper proposes a
doi:10.1016/j.datak.2008.03.004
fatcat:atfcrk3vkfgrfbwg4iin5daije