Mathematics – Statistics Theory
Scientific paper
2008-11-16
Journal of the American Statistical Association (2010) Volume 105, No. 489, pp 375--389
Mathematics
Statistics Theory
42 pages, 1 figure
Scientific paper
One goal of statistical privacy research is to construct a data release mechanism that protects individual privacy while preserving information content. An example is a {\em random mechanism} that takes an input database $X$ and outputs a random database $Z$ according to a distribution $Q_n(\cdot|X)$. {\em Differential privacy} is a particular privacy requirement developed by computer scientists in which $Q_n(\cdot |X)$ is required to be insensitive to changes in one data point in $X$. This makes it difficult to infer from $Z$ whether a given individual is in the original database $X$. We consider differential privacy from a statistical perspective. We consider several data release mechanisms that satisfy the differential privacy requirement. We show that it is useful to compare these schemes by computing the rate of convergence of distributions and densities constructed from the released data. We study a general privacy method, called the exponential mechanism, introduced by McSherry and Talwar (2007). We show that the accuracy of this method is intimately linked to the rate at which the probability that the empirical distribution concentrates in a small ball around the true distribution.
Wasserman Larry
Zhou Shuheng
No associations
LandOfFree
A statistical framework for differential privacy does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with A statistical framework for differential privacy, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A statistical framework for differential privacy will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-330768