Established practices for data privacy focus on simplistic transformations such as the removal of “personally identifiable information.” On the other hand, academia has produced a long line of work on privacy-preserving computation that has yet to be translated into practice. I envision privacy technologies as an engineering discipline grounded in a solid understanding of what technological mechanisms can and cannot do.

 

In this talk, I will describe my past, ongoing and planned work towards this goal. The first part of this research program — and the main topic of my doctoral work — has been to demonstrate the inadequacy of the current paradigm by developing reidentification and statistical inference algorithms for various types of “anonymized” data: our preferences, transactions, social relationships, and behavior. The second part is to develop an approach to building systems based on lightweight cryptography, a hybrid of centralized and decentralized architectures, and incorporation of policy-based defenses. I will describe how I have applied these principles to my work on location privacy and behavioral ad targeting.