Equitable differential privacy.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers in Big Data Pub Date : 2024-08-16 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1420344
Vasundhara Kaul, Tamalika Mukherjee
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Abstract

Differential privacy (DP) has been in the public spotlight since the announcement of its use in the 2020 U.S. Census. While DP algorithms have substantially improved the confidentiality protections provided to Census respondents, concerns have been raised about the accuracy of the DP-protected Census data. The extent to which the use of DP distorts the ability to draw inferences that drive policy about small-populations, especially marginalized communities, has been of particular concern to researchers and policy makers. After all, inaccurate information about marginalized populations can often engender policies that exacerbate rather than ameliorate social inequities. Consequently, computer science experts have focused on developing mechanisms that help achieve equitable privacy, i.e., mechanisms that mitigate the data distortions introduced by privacy protections to ensure equitable outcomes and benefits for all groups, particularly marginalized groups. Our paper extends the conversation on equitable privacy by highlighting the importance of inclusive communication in ensuring equitable outcomes for all social groups through all the stages of deploying a differentially private system. We conceptualize Equitable DP as the design, communication, and implementation of DP algorithms that ensure equitable outcomes. Thus, in addition to adopting computer scientists' recommendations of incorporating equity parameters within DP algorithms, we suggest that it is critical for an organization to also facilitate inclusive communication throughout the design, development, and implementation stages of a DP algorithm to ensure it has an equitable impact on social groups and does not hinder the redressal of social inequities. To demonstrate the importance of communication for Equitable DP, we undertake a case study of the process through which DP was adopted as the newest disclosure avoidance system for the 2020 U.S. Census. Drawing on the Inclusive Science Communication (ISC) framework, we examine the extent to which the Census Bureau's communication strategies encouraged engagement across the diverse groups of users that employ the decennial Census data for research and policy making. Our analysis provides lessons that can be used by other government organizations interested in incorporating the Equitable DP approach in their data collection practices.

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公平的差别隐私。
自从宣布在 2020 年美国人口普查中使用差分隐私 (DP) 后,它一直是公众关注的焦点。虽然 DP 算法大大提高了对人口普查受访者的保密保护,但受 DP 保护的人口普查数据的准确性也引起了关注。研究人员和政策制定者尤其关注的是,DP 的使用在多大程度上扭曲了对小群体,尤其是边缘化群体进行推论以推动政策制定的能力。毕竟,关于边缘化人群的不准确信息往往会导致政策加剧而非改善社会不平等。因此,计算机科学专家专注于开发有助于实现公平隐私的机制,即减轻隐私保护带来的数据扭曲的机制,以确保所有群体,特别是边缘化群体获得公平的结果和利益。我们的论文通过强调包容性交流在确保所有社会群体在部署差异化隐私系统的所有阶段都能获得公平结果方面的重要性,扩展了有关公平隐私的讨论。我们将公平 DP 概念化为确保公平结果的 DP 算法的设计、交流和实施。因此,除了采纳计算机科学家关于在 DP 算法中纳入公平参数的建议外,我们还建议组织在 DP 算法的整个设计、开发和实施阶段促进包容性沟通,以确保其对社会群体产生公平影响,且不妨碍纠正社会不公平现象,这一点至关重要。为了证明沟通对于公平 DP 的重要性,我们对 DP 被采纳为 2020 年美国人口普查最新的信息披露规避系统的过程进行了案例研究。借鉴包容性科学交流(ISC)框架,我们研究了人口普查局的交流策略在多大程度上鼓励了使用十年一次的人口普查数据进行研究和决策的不同用户群体的参与。我们的分析为其他有意将公平 DP 方法纳入其数据收集实践的政府组织提供了可借鉴的经验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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