Reduce Privacy Risks

There are at least two key features of the move to cloud computing that introduce the opportunity for significant leaps forward in computer security for tenant services. First, a compute cloud provides a common software, hardware and management basis for rolling out cross-cutting services en masse that have resisted incremental deployment in a one-service-at-a-time fashion. Second, compute clouds offer providers a broad view of activity across an unprecedented diversity of tenant services.

This project aims at developing efficient methods for protecting the privacy of computations on outsourced data in distributed settings. The project addresses the design of an outsourced storage framework where the access pattern observed by the storage server gives no information about the actual data accessed by the client and cannot be correlated with external events. For example, the server cannot determine whether a certain item was previously accessed by the client or whether a certain algorithm is being executed.

The collection and analysis of personal data about individuals has revolutionized information systems and fueled US and global economies. But privacy concerns regarding the use of such data loom large. Differential privacy has emerged as a gold standard for mathematically characterizing the privacy risks of algorithms using personal data. Yet, adoption of differentially private algorithms in industry or government agencies has been startlingly rare.