With the rapid increase in computing, storage and networking resources, data is not only collected and stored but also analyzed. This creates a serious privacy problem which often inhibits the use of this data. This project explores the problem of performing optimization analysis over distributed data without conflicting with privacy and security concerns. This is especially challenging due to the complexity and iterative nature of the solutions. An inherent aim is to also solve some of the fundamental problems underlying privacy-preserving analysis / secure computation and make it more accessible and applicable. Some of the innovative expected results include: (1) novel formulations of security definitions that are more relaxed than the traditional definitions yet still model the real security concerns; (2) new algorithms, computational complexity results, and tools for specific widely used optimization problems; (3) a more generalized view of privacy; (4) game theoretic interpretations and modeling of the multi-party computation; and (5) result analysis ? a quantification of privacy loss through results. The project will have tremendous broader impact via fundamental research and integrative education. Direct outcomes of the research can significantly help in widening co-operation between organizations and minimize loss through data isolation. This would result in cost savings and new income realization potentially worth billions of dollars through joint resource usage. Translation of the research to real use has the potential to revolutionize the mediator/consolidator industry. The integrative education activities will foster actual use of the technology and open up its acceptance into the real world.
CAREER: Collaborative Optimization with Limited Information Disclosure