Differential Privacy Overview and Fundamental Techniques

TL;DR

This chapter introduces differential privacy, discussing its importance, core principles, formal definitions, properties, and fundamental techniques for privacy-preserving data analysis in artificial intelligence.

Contribution

It provides a comprehensive overview of differential privacy, highlighting its formal foundations, key properties, and basic mechanisms for implementation.

Findings

Formal definition of differential privacy and its properties

Review of basic techniques and mechanisms for differential privacy

Analysis of privacy-preserving data analysis tasks

Abstract

This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It starts by illustrating various attempts to protect data privacy, emphasizing where and why they failed, and providing the key desiderata of a robust privacy definition. It then defines the key actors, tasks, and scopes that make up the domain of privacy-preserving data analysis. Following that, it formalizes the definition of Differential Privacy and its inherent properties, including composition, post-processing immunity, and group privacy. The chapter also reviews the basic techniques and mechanisms commonly used to implement Differential Privacy in its pure and approximate forms.