SPAR

Security and Privacy Applied Research Lab

Towards Rigorous Foundations for Database Privacy

Adam Smith
Pennsylvania State University

Abstract

Collections of personal and sensitive data, previously the purview of governments and statistical agencies, have become ubiquitous. The social benefits of analyzing these databases are significant: better informed policy decisions, more efficient markets, and more accurate public health data, to name a few. At the same time, releasing information from repositories of sensitive data can cause devastating damage to the privacy of individuals or organizations whose information is stored there. The challenge is to discover and release global characteristics of these databases, while protecting the privacy of individuals' records.

I will discuss a recent line of work exploring the tradeoff between these conflicting goals -- first, how the goals can be formulated precisely and second, to what extent they can both be satisfied.

I will explain why many popular approaches to data privacy fail to protect privacy in the presence of even very simple auxiliary information. In contrast, I will explain how a large class of computations can be performed while providing meaningful privacy guarantees, in the presence of *arbitrary* auxiliary information.

This is based on several works, joint with (subsets of) Cynthia Dwork, Ranjit Ganta, Shiva Kasiviswanathan, Homin Lee, Frank McSherry, Kobbi Nissim, and Sofya Raskhodnikova.