Advancing differential privacy: where we are now and future directions for real-world deployment - Cummings, Rachel; Desfontaines, Damien; Evans, David; Geambasu, Roxana; Huang, Yangsibo; Jagielski, Matthew; Kairouz, Peter; Kamath, Gautam; Oh, Sewoong; Ohrimenko, Olga; Papernot, Nicolas; Rogers, Ryan; et al. (Harvard Data Science Review, 2024)
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP’s deployment in real-world applications. Key points and high-level contents of the article were originated from the discussions from “Differential Privacy (DP): Challenges Towards the Next Frontier,” a workshop held in July 2022 with experts from industry, academia, and the public sector seeking answers to broad questions pertaining to privacy and its implications in the design of industry-grade systems. This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions. Covering a wide spectrum of topics, this article delves into the infrastructure needs for designing private systems, methods for achieving better privacy/utility trade-offs, performing privacy attacks and auditing, as well as communicating privacy with broader audiences and stakeholders.