Understanding Risks of Privacy Theater with Differential Privacy-Reference-Cited by-同舟云学术

Understanding Risks of Privacy Theater with Differential Privacy

Published:2022-11-07 Issue:CSCW2 Volume:6 Page:1-24
ISSN:2573-0142
Container-title:Proceedings of the ACM on Human-Computer Interaction
language:en
Short-container-title:Proc. ACM Hum.-Comput. Interact.

Author:

Smart Mary Anne¹,Sood Dhruv¹,Vaccaro Kristen¹

Affiliation:

1. University of California San Diego, San Diego, CA, USA

Abstract

Differential privacy is one of the most popular technologies in the growing area of privacy-conscious data analytics. But differential privacy, along with other privacy-enhancing technologies, may enable privacy theater. In implementations of differential privacy, certain algorithm parameters control the tradeoff between privacy protection for individuals and utility for the data collector; thus, data collectors who do not provide transparency into these parameters may obscure the limited protection offered by their implementation. Through large-scale online surveys, we investigate whether explanations of differential privacy that hide important information about algorithm parameters persuade users to share more browser history data. Surprisingly, we find that the explanations have little effect on individuals' willingness to share data. In fact, most people make up their minds about whether to share before they even learn about the privacy protection.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)

Link

https://dl.acm.org/doi/pdf/10.1145/3555762

Reference89 articles.

1. The U.S. Census Bureau Adopts Differential Privacy

2. Your Location has been Shared 5,398 Times!

3. Mike Ananny and Kate Crawford . 2018. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. new media & society , Vol. 20 , 3 ( 2018 ), 973--989. Mike Ananny and Kate Crawford. 2018. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. new media & society, Vol. 20, 3 (2018), 973--989.

4. Eduardo B Andrade , Velitchka Kaltcheva , and Barton Weitz . 2002. Self-disclosure on the web: The impact of privacy policy, reward, and company reputation. ACR North American Advances ( 2002 ). Eduardo B Andrade, Velitchka Kaltcheva, and Barton Weitz. 2002. Self-disclosure on the web: The impact of privacy policy, reward, and company reputation. ACR North American Advances (2002).

5. Decolonizing Privacy Studies

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