Randomization in privacy preserving data mining

Abstract

Suppose there are many clients, each having some personal information, and one server, which is interested only in aggregate, statistically significant, properties of this information. The clients can protect privacy of their data by perturbing it with a randomization algorithm and then submitting the randomized version. The randomization algorithm is chosen so that aggregate properties of the data can be recovered with sufficient precision, while individual entries are significantly distorted. How much distortion is needed to protect privacy can be determined using a privacy measure. Several possible privacy measures are known; finding the best measure is an open question. This paper presents some methods and results in randomization for numerical and categorical data, and discusses the issue of measuring privacy.