Contextual Integrity through the Lens of Computer Science

Abstract

In this work, we propose the first framework for integrating Differential Privacy (DP) and Contextual Integrity (CI). DP is a property of an algorithm that injects statistical noise to obscure information about individuals represented within a database. CI defines privacy as information flow that is appropriate to social context. Analyzed together, these paradigms outline two dimensions on which to analyze privacy of information flows: descriptive and normative properties. We show that our new integrated framework provides benefits to both CI and DP that cannot be attained when each definition is considered in isolation: it enables contextuallyguided tuning of the ǫ parameter in DP, and it enables CI to be applied to a broader set of information flows occurring in real-world systems, such as those involving PETs and machine learning. We conclude with a case study based on the use of DP in the U.S. Census Bureau.

`I make up a silly name'

“…The CI framework provides a useful philosophical framework for understanding the social and ethical aspects of privacy. However, translating it into a technical implementation is not straightforward [12]. Most of the previous work has used context as a synonym for a scenario or situation, to understand users' privacy preferences and expectations [75,80].…”

Contextual Integrity, Explained: A More Usable Privacy Definition

“…CI has already been used successfully in a wide variety of computing research projects and beyond. 5,6,7,8 The rest of this article explains this theory and shows how to apply it.…”

“…In the meantime, the theory has already proven useful in a number of computer science research projects. 5 Still, CI isn't the final word in privacy; it has a number of limitations, which are worth knowing about.…”

GKC‐CI: A unifying framework for contextual norms and information governance

“…CI offers a structured way to ask normative questions and reason about the privacy and ethical implications of information flows produced or regulated by new technologies or policies. In recent years, a large body of work (Badillo-Urquiola et al, 2018;Benthall et al, 2017) has turned to the CI theory and framework to inform study design and results analysis to understand users' privacy expectations. Wijesekera et al (2015) used CI to conduct a field study to analyze cases where users' expectations established by the Android mobile operating system permission model were violated by the underlying system.…”