Adoption of 'Privacy-Preserving' Analytics: Drivers, Designs, & Decoupling

45 Pages Posted: 19 Feb 2024

See all articles by Ryan Steed

Ryan Steed

Carnegie Mellon University - H. John Heinz III College of Information Systems and Public Policy; Carnegie Mellon University - Machine Learning Department

Alessandro Acquisti

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Date Written: February 7, 2024

Abstract

Techniques for privacy-preserving analytics (PPA) offer organizations a way to maintain and expand access to valuable data while preserving individuals’ privacy. Adoption of PPA is growing in industry and government, but the impacts are not yet clear: small differences in design can have significant downstream impacts on data privacy, and little research examines the decisions that determine whether adoption lives up to consumers’ and regulators’ expectations. We investigate the organizational processes driving adoption and deployment of PPA systems in a qualitative study based on interviews with the executives, lawyers, engineers, and scientists implementing PPA across 21 large technology firms, startups, non-profits, and government agencies. We develop a grounded theory of the drivers of adoption, the ways practitioners interpret those drivers into specific designs, and the ways organizations justify their design choices. We find that organizations a) prioritize managerial concerns over privacy in PPA design, b) decouple representations about privacy from the specifics of their implementation, and c) use PPA adoption to influence privacy expectations in turn. However, we also find that morally motivated practitioners leverage their expertise to spread adoption and maintain their own standards. We explore the consequences of these findings for the future of regulation and research.

Keywords: privacy-preserving technology, technology adoption, organizational decoupling, legal endogeneity, algorithmic systems

Suggested Citation

Steed, Ryan and Acquisti, Alessandro, Adoption of 'Privacy-Preserving' Analytics: Drivers, Designs, & Decoupling (February 7, 2024). Available at SSRN: https://ssrn.com/abstract=4718865 or http://dx.doi.org/10.2139/ssrn.4718865

Ryan Steed (Contact Author)

Carnegie Mellon University - H. John Heinz III College of Information Systems and Public Policy ( email )

Pittsburgh, PA
United States

Carnegie Mellon University - Machine Learning Department ( email )

Gates Hillman Center
5000 Forbes Ave 8th
Pittsburgh, PA 15213-3891
United States

Alessandro Acquisti

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States
412-268-9853 (Phone)
412-268-5339 (Fax)

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