Natural language privacy policies have become a de facto standard to address expectations of “notice and choice” on the Web. Yet, there is ample evidence that users generally do not read these policies and that those who occasionally do struggle to understand what they read. Initiatives aimed at addressing this problem through the development of machine implementable standards or other solutions that require website operators to adhere to more stringent requirements have run into obstacles, with many website operators showing reluctance to commit to anything more than what they currently do.
This frontier project builds on recent advances in natural language processing, privacy preference modeling, crowdsourcing, formal methods, and privacy interfaces to overcome this situation. It combines fundamental research with the development of scalable technologies to:
(2) present these features to users in an easy-to-digest format that enables them to make more informed privacy decisions as they interact with different websites.
As such, this project offers the prospect of overcoming the limitations of current natural language privacy policies without imposing new requirements on website operators.
Work in this project will also involve the systematic collection and analysis of website privacy policies, looking for trends and deficiencies both in the wording and content of these policies across different sectors and using this analysis to inform ongoing public policy debates.
A transition phase will enable the transfer of these technologies to industry for large-scale deployment and to regulators and policy makers interested in tracking practices.