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Optimizing Privacy-Accuracy Tradeoff for Privacy Preserving Distance-Based Classification
Privacy concerns often prevent organizations from sharing data for data mining purposes. There has been a rich literature on privacy preserving data mining techniques that can protect privacy and still allow accurate mining. Many such techniques have some parameters that need to be set correctly to achieve the desired balance between privacy protection and quality of mining results. However, there has been little research on how to tune these parameters effectively. This paper studies thedoi:10.13016/m2jng5-waqt fatcat:wu4v2v35tve3vp2zikh7szuwd4