Scalable Algorithms for Protecting User Privacy

Lecture / Panel
For NYU Community

Speaker: Aleksandra Korolova


Privacy challenges have reached new heights thanks to the ubiquitous use of Internet and mobile technologies combined with dropping data storage and processing costs. At the same time, the abundance of digital data has opened the door to new and useful privacy-preserving computations and technologies. In this talk, I will describe several examples of such computations. I will present an algorithm for search data release with provable privacy guarantees, show how well-targeted ads can reveal users' secrets, and how random projections can help protect them. I will also describe how the same data mining techniques used to improve search can be applied towards improving privacy policies and tools.


Aleksandra Korolova is a research scientist at Google, where she works on developing and implementing approaches for privacy-preserving data analysis and for data-driven study of user privacy preferences. She received her Ph.D. in Computer Science from Stanford, where she was advised by Prof. Rajeev Motwani and Prof. Ashish Goel. Aleksandra's thesis, "Protecting Privacy when Mining and Sharing User Data", was awarded the Arthur L. Samuel Award for the best 2011-2012 CS Ph.D. thesis at Stanford, and her work on "Privacy Violations Using Microtargeted Ads" was a co-winner of the 2011 PET Award for Outstanding Research in Privacy Enhancing Technologies.Aleksandra was a Cisco Systems Stanford Graduate Fellow in 2008-2010, and has graduated Phi Beta Kappa from MIT with a B.S. degree in Mathematics with Computer Science.