Going against the (Appropriate) Flow: A Contextual Integrity Approach to Privacy Policy Analysis


  • Yan Shvartzshnaider New York University
  • Noah Apthorpe Princeton University
  • Nick Feamster University of Chicago
  • Helen Nissenbaum Cornell Tech




We present a method for analyzing privacy policies using the framework of contextual integrity (CI). This method allows for the systematized detection of issues with privacy policy statements that hinder readers’ ability to understand and evaluate company data collection practices. These issues include missing contextual details, vague language, and overwhelming possible interpretations of described information transfers. We demonstrate this method in two different settings. First, we compare versions of Facebook’s privacy policy from before and after the Cambridge Analytica scandal. Our analysis indicates that the updated policy still contains fundamental ambiguities that limit readers’ comprehension of Facebook’s data collection practices. Second, we successfully crowdsourced CI annotations of 48 excerpts of privacy policies from 17 companies with 141 crowdworkers. This indicates that regular users are able to reliably identify contextual information in privacy policy statements and that crowdsourcing can help scale our CI analysis method to a larger number of privacy policy statements.




How to Cite

Shvartzshnaider, Y., Apthorpe, N., Feamster, N., & Nissenbaum, H. (2019). Going against the (Appropriate) Flow: A Contextual Integrity Approach to Privacy Policy Analysis. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7(1), 162-170. https://doi.org/10.1609/hcomp.v7i1.5266