Feb 16, 2018
Lopata Hall, Room 101
Privacy policies are intended to notify Internet users of organizations’ privacy practices and give them choices to opt out from behavioral advertising and other services. However, very few users actually read privacy policies and many remain oblivious to what happens to their data. In addition, software developers are oftentimes not aware of their legal obligations and fail to disclose their privacy practices. These cases of non-compliance can remain undetected for extended periods of time as the Federal Trade Commission and other privacy regulators do not have the resources to perform their oversight systematically and comprehensively. The use of machine learning technologies to analyze privacy policies automatically for compliance with privacy law requirements holds promise to alleviate these problems.
Sebastian Zimmeck is a postdoc in computer science at Carnegie Mellon University's Institute for Software Research. His research interests are Internet privacy and security. Before coming to Carnegie Mellon Sebastian studied computer science at Columbia University. He also studied information privacy and intellectual property law and practiced in these areas as an attorney with international law firm Freshfields Bruckhaus Deringer. He was a Google Research Fellow at the Berkeley Center for Law & Technology. Sebastian holds degrees in computer science from Columbia University (MS, PhD) as well as law degrees from the University of California, Berkeley (LLM) and the University of Kiel (JD, PhD). He is licensed to practice law in California and Germany (both admissions currently inactive).