Kaul Vasundhara, Mukherjee Tamalika
Department of Sociology, Purdue University, West Lafayette, IN, United States.
Industrial Engineering and Operations Research, Columbia University, New York, NY, United States.
Front Big Data. 2024 Aug 16;7:1420344. doi: 10.3389/fdata.2024.1420344. eCollection 2024.
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.
自2020年美国人口普查宣布使用差分隐私（DP）以来，它一直处于公众关注的焦点。虽然DP算法大大提高了为人口普查受访者提供的保密保护，但人们对DP保护的人口普查数据的准确性提出了担忧。DP的使用在多大程度上扭曲了对小群体（尤其是边缘化社区）进行政策推断的能力，这一直是研究人员和政策制定者特别关注的问题。毕竟，关于边缘化人群的不准确信息往往会导致加剧而非缓解社会不平等的政策。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，即减轻隐私保护引入的数据扭曲以确保所有群体（尤其是边缘化群体）获得公平结果和利益的机制。我们的论文通过强调包容性沟通在通过部署差分隐私系统的所有阶段确保所有社会群体获得公平结果方面的重要性，扩展了关于公平隐私的讨论。我们将公平差分隐私概念化为确保公平结果的差分隐私算法的设计、沟通和实施。因此，除了采纳计算机科学家在差分隐私算法中纳入公平参数的建议外，我们还建议，对于一个组织来说，在差分隐私算法的设计、开发和实施阶段促进包容性沟通也至关重要，以确保其对社会群体产生公平影响，且不妨碍纠正社会不平等现象。为了证明沟通对于公平差分隐私的重要性，我们对差分隐私被采用为2020年美国人口普查最新披露规避系统的过程进行了案例研究。借鉴包容性科学传播（ISC）框架，我们研究了人口普查局的沟通策略在多大程度上鼓励了使用十年一次人口普查数据进行研究和政策制定的不同用户群体之间参与互动。我们的分析提供了可供其他有兴趣在其数据收集实践中纳入公平差分隐私方法的政府组织借鉴的经验教训。
