June 27, 2017

Learning Privacy Expectations by Crowdsourcing Contextual Informational Norms

[This post reports on joint work with Schrasing Tong, Thomas Wies (NYU), Paula Kift (NYU), Helen Nissenbaum (NYU), Lakshminarayanan Subramanian (NYU), Prateek Mittal (Princeton) — Yan]

To appear in the proceedings of the Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2016)

We would like to thank Joanna Huey for helpful comments and feedback.

Motivation

The advent of social apps, smart phones and ubiquitous computing has brought a great transformation to our day-to-day life. The incredible pace with which the new and disruptive services continue to emerge challenges our perception of privacy. To keep apace with this rapidly evolving cyber reality, we need to devise agile methods and frameworks for developing privacy-preserving systems that align with evolving user’s privacy expectations.

Previous efforts [1,2,3] have tackled this with the assumption that privacy norms are provided through existing sources such law, privacy regulations and legal precedents. They have focused on formally expressing privacy norms and devising a corresponding logic to enable automatic inconsistency checks and efficient enforcement of the logic.

However, because many of the existing regulations and privacy handbooks were enacted well before the Internet revolution took place, they often lag behind and do not adequately reflect the application of logic in modern systems. For example, the Family Rights and Privacy Act (FERPA) was enacted in 1974, long before Facebook, Google and many other online applications were used in an educational context. More recent legislation faces similar challenges as novel services introduce new ways to exchange information, and consequently shape new, unconsidered information flows that can change our collective perception of privacy.

Crowdsourcing Contextual Privacy Norms

Armed with the theory of Contextual Integrity (CI) in our work, we are exploring ways to uncover societal norms by leveraging the advances in crowdsourcing technology.  

In our recent paper, we present the methodology that we believe can be used to extract a societal notion of privacy expectations. The results can be used to fine tune the existing privacy guidelines as well as get a better perspective on the users’ expectations of privacy. [Read more…]