An Introduction to Differential Privacy for Analysis of Sensitive Data
Channel: CPI Waterloo
Date: 2023-05-18

Sensitive data is increasingly ubiquitous in modern applications, and its protection is a critical concern for both individuals and organizations. For example, in the healthcare industry, vast amounts of sensitive data are collected and analyzed to improve patient outcomes, identify disease trends, and develop new treatments. However, any analyses run on this must be secured to prevent inadvertent disclosure of sensitive data. Best effort or heuristic approaches such as anonymization and data minimization have been repeatedly demonstrated to be ineffective. One promising solution to this challenge is differential privacy, a technique that provides a mathematically rigorous guarantee of privacy protection while still allowing for meaningful analysis of the underlying data. Differential privacy has already been implemented and deployed in practice in a number of settings. Some notable examples include by the US Census Bureau in the 2020 US Census, and by Apple and Google to gather and analyze aggregate user behaviour and telemetry. In this talk, we will provide an introduction to differential privacy and explore how it can be used to analyze sensitive data, simultaneously providing useful results and rigorous privacy guarantees for the individuals who provided their data. We will discuss the basic principles of data privacy, the failures of heuristic or best-effort approaches, the notion of differential privacy, and methods to make data analysis procedures differentially private.