The treatment of privacy in data analysis has taken a dramatic shift a little more than a decade ago - as failures of traditional privacy preserving techniques were beginning to accumulate, a theoretical, foundational approach to privacy emerged. A key product of this theoretical treatment is "differential privacy", a definition of privacy in the context of data analysis that has concrete privacy consequences.
Differential privacy became to be a rich, fast evolving framework for developing privacy preserving algorithms and for studying some of the fundamental properties of privacy. Moreover, differential privacy proved to interact fruitfully with other research areas, and to influence applications that are (seemingly) not related to privacy. With a mature theoretical basis, differential privacy is now at prime time for inclusion in real-world systems.
We will look into the intuition behind differential privacy, review some of is theory, and examine some of the challenges for applying differential privacy.
The talk would be self-contained, no prior background on privacy would be assumed.
Bio:
Kobbi Nissim is a Professor of Computer Science at Ben-Gurion University and a Senior Research Fellow at the Center for Research on Computation and Society at Harvard. Trained in cryptography, Kobbi maintains a healthy level of paranoia, and feels the ground is shaky whenever issues of security and privacy are not formally defined and analysed.
Kobbi's current work is focused on the theory and application of differential privacy. His work from 2003 and 2004 with Dinur and Dwork initiated rigorous foundational research of privacy and presented a precursor of Differential Privacy, a strong definition of privacy in computation that he introduced in 2006 with Dwork, McSherry and Smith. With collaborators, Nissim established some of the basic constructions supporting differential privacy, and studied differential privacy and its relationships with cryptography, statistics, computational learning, mechanism design, and social networks as well as policy and regulation. Since 2011, Kobbi has been involved with the Privacy Tools for Sharing Research Data project at Harvard University, developing privacy-preserving tools for the sharing of social science data. Other contributions of Nissim include the BGN homomorphic encryption scheme with Boneh and Goh, and the research of private approximations.
In 2013, Nissim received with Irit Dinur the ACM Alberto O. Mendelzon Test-of-Time award for their PODS 2003 paper initializing the foundational work on privacy. In 2016 he will receive with Dwork, McSherry, and Smith the IACR TCC Test-of-Time award for their TCC 2006 paper introducing differential privacy.