Paper 2017/882

Towards an in-depth understanding of privacy parameters for randomized sanitization mechanisms

Baptiste Olivier and Tony Quertier

Abstract

Differential privacy, and close other notions such as $d_{\chi }$-privacy, is at the heart of the privacy framework when considering the use of randomization to ensure data privacy. Such a guarantee is always submitted to some trade-off between the privacy level and the accuracy of the result. While a privacy parameter of the differentially private algorithms leverages this trade-off, it is often a hard task to choose a meaningful value for this numerical parameter. Only a few works have tackled this issue, and the present paper's goal is to continue this effort in two ways. First, we propose a generic framework to decide whether a privacy parameter value is sufficient to prevent from some pre-determined and well-understood risks for privacy. Second, we instantiate our framework on mobility data from real-life datasets, and show some insightful features necessary for practical applications of randomized sanitization mechanisms. In our framework, we model scenarii where an attacker's goal is to de-sanitize some data previously sanitized in the sense of $$-privacy, a privacy guarantee close to that of differential privacy. To each attack is associated a meaningful risk of data disclosure, and the level of success for the attack suggests a relevant value for the corresponding privacy parameter.