Locating a Small Cluster, Privately
Kobbi Nissim, Salil Vadhan, Uri Stemmer · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · 2016
We present a new algorithm for locating a small cluster of points with differential privacy [Dwork, McSherry, Nissim, and Smith, 2006]. Our algorithm has implications to private data exploration, clustering, and removal of outliers. Furthermore, we use it to significantly relax the requirements of the sample and aggregate technique [Nissim, Raskhodnikova, and Smith, 2007], which allows compiling of “off the shelf” (non-private) analyses into analyses that preserve differential privacy.