IACR News item: 11 October 2014
Jalaj Upadhyay
ePrint ReportOur proof for both the privacy and utility guarantee uses several new ideas. In order to improve the dimension bound, we use some known results from the domain of statistical model selection. This makes our proof short and elegant, relying just on one basic concentration inequality. For the privacy proof, even though our mechanism closely resembles that of Blocki {\\it et al.} (FOCS 2012) and Upadhyay (ASIACRYPT 2013), we cannot use their proof idea. This is because the projection matrices we are interested in introduces non-trivial correlations between any two rows of the published matrix, and, therefore, we cannot invoke the composition theorem of Dwork, Rothblum and Vadhan (STOC 2009). We argue that the published matrix is not $r$-multivariate distribution; rather one matrix-variate distribution. We compute the distribution of the published matrix and then prove it preserves differential privacy.
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