From Algorithmic to Institutional Logics: The Politics of Differential Privacy

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Over the past two decades, we have come to see that traditional de-anonymization techniques fail to protect the privacy of individuals in sensitive datasets. To address this problem, computer scientists introduced differential privacy, a strong statistical notion of privacy that bounds the amount of information a statistical release leaks about any individual. Differential privacy has become a gold standard for privacy protection: organizations from Google to the U.S. Census Bureau have adopted differentially private methods, and the MIT Technology Review named it as one of the top ten technologies expected to have “widespread consequences for human life.” Yet, while differential privacy offers rigorous statistical guarantees, we must also examine how these guarantees interact with social and contextual factors. In this paper, I investigate the political dimensions of differential privacy. What does the adoption of this standard reveal or obscure about the privacy practices within our sociotechnical systems? And how might a reliance on this standard impact our progress towards broader notions of privacy? Drawing on scholarship from sociology, law, computer science, and science and technology studies, I describe the entanglements between algorithmic privacy and institutional logics, highlighting disempowering practices that may emerge despite, or in response to, the adoption of differential privacy. The goal of this work is not to discourage the use of differential privacy, which I argue is necessary and beneficial in a wide range of settings, but to examine where it may have unintended consequences. I conclude with recommendations on how the privacy community can continue to develop formal privacy standards while elevating broader visions of privacy.