Keynote: Machine Learning and Privacy: Challenges and Opportunities
The emergence of powerful machine learning methods presents both challenges and opportunities for data privacy research. On the one hand, machine learning models trained on sensitive data present new privacy risks and open the door to new types of inference attacks. On the other hand, many objectives of modern machine learning – in particular, constructing generalizable models that are not overfitted to the training data – are compatible with privacy and benefit from the same set of techniques. In this talk, I will discuss several open research questions at the junction of machine learning and privacy.
About Vitaly Shmatikov:
Vitaly Shmatikov is a professor at Cornell Tech, where he works on computer security and privacy. Vitaly received the PET Award for Outstanding Research in Privacy Enhancing Technologies twice, in 2008 and 2014, and was a runner-up in 2013. His research group won the Best Practical Paper or Best Student Paper Awards at the 2012, 2013, and 2014 IEEE Symposiums on Security and Privacy (“Oakland”), as well as the 2012 NYU-Poly AT&T Best Applied Security Paper Award, NDSS 2013 Best Student Paper Award, and the CCS 2011 Test-of-Time Award.