Defining Privacy-Preserving Data Analysis

Abstract

With the growing focus on instrumenting our environment and monitoring our activities, there is a need to implement privacy-preserving algorithms into our technological systems. Defining privacy formally is a delicate task but is a necessary first step to be able to provide clear guarantees to the individuals being monitored. In this chapter, after discussing the pitfalls of naive approaches to data privacy, we review the notion of differential privacy, a state-of-the-art definition of privacy that we adopt in the rest of this monograph, and which provides guarantees against adversaries with arbitrary side information. Privacy-preserving data analysis has a relatively long history in fields such as econometrics and statistics or for the processing of sensitive static data stored for example in medical databases. Current trends emphasize the need to work with streams of data originating from many sources and requiring sanitization in real-time, which brings new challenges to the field.