CAREER: The Algorithmic Foundations of Data Privacy

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The past decade has seen a growing reliance on data driven technologies, including recommendation systems, targeted advertising, and search personalization. This growth in big data has made data privacy into a central concern. The central question raised is: how can we continue to extract useful information from large datasets, while provably protecting some measure of privacy for the individuals contained in these datasets?This research centers around advancing the state of the art in privacy preserving data analysis. It specifically has several themes: (1) Exploiting structure in the private data being analyzed, as well as the classes of queries used in the analysis to give computationally efficient algorithms for private data analysis. (2) Deepening the connections between private data analysis and machine learning theory. (3) Relaxing the adversarial collusion model implicit in most work on the foundations of data privacy, and (4) applying the tools of differential privacy to usefully exploit and analyze noise in other algorithmic settings. To ensure the broad impact of this research, this project includes substantial outreach activities, including workshop organization, course development, and the development of a textbook and other educational materials.