@article{cummings2024advancing,
author = {Cummings, Rachel and Desfontaines, Damien and Evans, David and Geambasu, Roxana and Huang, Yangsibo and Jagielski, Matthew and Kairouz, Peter and Kamath, Gautam and Oh, Sewoong and Ohrimenko, Olga and Papernot, Nicolas and Rogers, Ryan and Shen, Milan and Song, Shuang and Su, Weijie and Terzis, Andreas and Thakurta, Abhradeep and Vassilvitskii, Sergei and Wang, Yu-Xiang and Xiong, Li and Yu, Da and Yekhanin, Sergey and Zhang, Huanyu and Zhang, Wanrong},
title = {Advancing differential privacy: where we are now and future directions for real-world deployment},
year = {2024},
month = {January},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/challenges-towards-the-next-frontier-in-privacy/},
journal = {Harvard Data Science Review},
volume = {6},
number = {1},
}