Learning Privacy Expectations by Crowdsourcing Contextual Informational Norms

Yan Shvartzshnaider, Schrasing Tong, Thomas Wies, Paula Kift, Helen Nissenbaum, Lakshminarayanan Subramanian, Prateek Mittal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

Abstract

Designing programmable privacy logic frameworks that correspond to social, ethical, and legal norms has been a fundamentally hard problem. Contextual integrity (CI) (Nissenbaum 2010) 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, it is challenging to discover and formally express these norms in operational terms. In the following, we propose a crowdsourcing method for the automated discovery of contextual norms. To evaluate the effectiveness and scalability of our approach, we conducted an extensive survey on Amazon's Mechanical Turk (AMT) with more than 450 participants and 1400 questions. The paper has three main takeaways: First, we demonstrate the ability to generate survey questions corresponding to privacy norms within any context. Second, we show that crowdsourcing enables the discovery of norms from these questions with strong majoritarian consensus among users. Finally, we demonstrate how the norms thus discovered can be encoded into a formal logic to automatically verify their consistency.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2016
EditorsArpita Ghosh, Matthew Lease
PublisherAAAI press
Pages209-218
Number of pages10
ISBN (Electronic)9781577357742
StatePublished - Nov 3 2016
Event4th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2016 - Austin, United States
Duration: Oct 30 2016Nov 3 2016

Publication series

NameProceedings of the 4th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2016

Conference

Conference4th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2016
Country/TerritoryUnited States
CityAustin
Period10/30/1611/3/16

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Human-Computer Interaction

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