Incentive-Compatible Privacy-Preserving Distributed Data Mining

The quantity of data that is captured, collected, and stored by a wide variety of organizations is growing at an exponential rate. The potential for such data to support scientific discovery and optimization of existing systems is significant, but only if it can be integrated and analyzed in a meaningful way by a wide range of investigators. While many believe that data sharing is desirable, there are also privacy and security concerns, rooted in ethics and the law that often prevent many legitimate and noteworthy applications. In this talk, we will provide an overview on research regarding how to integrate and mine large amounts of privacy-sensitive distributed data without violating such constraints. Especially, we will discuss how to incentivize data sharing in privacy-preserving distributed data mining applications. This work will draw upon examples form the biomedical domain and discuss recent research on privacy preserving mining of genomic databases.