Advancing differential privacy: where we are now and future directions for real-world deployment
- Rachel Cummings ,
- Damien Desfontaines ,
- David Evans ,
- Roxana Geambasu ,
- Yangsibo Huang ,
- Matthew Jagielski ,
- Peter Kairouz ,
- Gautam Kamath ,
- Sewoong Oh ,
- Olga Ohrimenko ,
- Nicolas Papernot ,
- Ryan Rogers ,
- Milan Shen ,
- Shuang Song ,
- Weijie Su ,
- Andreas Terzis ,
- Abhradeep Thakurta ,
- Sergei Vassilvitskii ,
- Yu-Xiang Wang ,
- Li Xiong ,
- Da Yu ,
- Sergey Yekhanin ,
- Huanyu Zhang ,
- Wanrong Zhang
Harvard Data Science Review | , Vol 6(1)
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.