Smooth sensitivity and sampling in private data analysis

We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains. Our framework allows one to release functions f of the data with instance-based additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also by the database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smooth sensitivity of f on the database x -- a measure of variability of f in the neighborhood of the instance x. The new framework greatly expands the applicability of output perturbation, a technique for protecting individuals' privacy by adding a small amount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect ...

• Type: Conference
• Year: 2007
• Where: STOC
• Authors: Kobbi Nissim, Sofya Raskhodnikova, Adam Smith
• Keywords: Algorithms, Noise Magnitude, Private Data Analysis, Smooth Sensitivity, STOC 2007