Abstract Bart Knijnenburg
Privacy issues are an undying obstacle to the real-world implementation of data science. While there exist several privacy-preserving data science solutions (e.g. client-side personalization, homomorphic encryption, k-anonymity), the concept of privacy is an inherently human attitude associated with the collection, distribution and use of disclosed data, and this disclosure itself is also a human behavior.
This talk discusses one particular human-centric solution to reduce users’ privacy concerns: User-Tailored Privacy. User-Tailored Privacy is an approach to privacy that measures users’ privacy-related characteristics and behaviors, uses this as input to model their privacy preferences, and then provides them with adaptive privacy decision support. In effect, it applies data science as a means to support users’ privacy decisions.