Crowdsourcing Verifiable Contextual Integrity Norms

Designing programmable privacy logic frameworks that correspond to social, ethical and legal norms has been a fundamentally hard problem. Contextual integrity (CI) [1] offers a model for conceptualizing privacy that is able to bridge technical design with ethical, legal, and policy approaches. While CI is capable of capturing the various components of contextual privacy in theory, no framework thus far has captured, encoded and verifiably enforced these norms in practice. Our paper proposes a novel privacy-preserving framework for crowdsourcing majoritarian privacy norms that are based on the language of CI. By conducting an extensive number of surveys on Amazon’s Mechanical Turk (AMT), we demonstrate that crowdsourcing can provide a meaningful approach for extracting a significant percentage of operational informational norms based on majoritarian consensus. These norms are then encoded using Datalog logic specification to develop an information system, which can verify whether information follows are appropriate – and the privacy of users thus preserved – on the basis of the theory of CI.