Integrating Differential Privacy and Contextual Integrity

Friday, June 24, 2022 - 9:15 am9:30 am

Sebastian Benthall, New York University


Differential privacy (DP) is an algorithmic privacy technique that incorporates noise parameters and probabilistic uncertainty, but provides little to no guidance as to the choice of these model parameters in practice. Contextual integrity (CI) instead theorizes privacy as appropriate information flow based on norms that inhere in social contexts. We propose a hybrid theory of DP and CI that can better inform privacy by design in practice. We augment the CI framework with an additional information norm parameter, transmission property, which denotes the quantitative form of the information flow, such as “with differential privacy” or “with 95% confidence”. We use this method to develop a way to define assumptions about contextual purposes and societal values, and to solve for the optimal information norms. DP’s continuous information design can support the purposes of some social spheres better than the coarse-grained information flows understood by CI. We apply this framework to three cases: the U.S. census, medical data sharing, and federated learning.

Sebastian Benthall, New York University

Dr. Sebastian Benthall is a Senior Research Fellow at the Information Law Institute at New York School of Law, as well as National Science Foundation Postdoctoral Research Fellow in Social, Behavioral, and Economic Sciences.

@conference {280276,
author = {Sebastian Benthall},
title = {Integrating Differential Privacy and Contextual Integrity},
year = {2022},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = jun

Presentation Video