CAREER: A Utility Aware Framework for Privately Sharing Individual Level Data

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Individual level data can be collected and shared to benefit a wide range of research studies and exploratory applications. However, most privacy protecting data sharing solutions focus on well-defined aggregate analysis and do not take into account the application’s utility goals. Furthermore, current solutions may be limited in addressing the application-specific privacy needs, resulting in overly strong or inadequate privacy protection.This project develops novel privacy protecting data sharing solutions by incorporating an application’s privacy needs and utility goals in one optimization framework. The framework is compatible with a variety of rigorous privacy models. More importantly, it allows the application to customize the privacy mechanism’s structure as well as fine-grained output utility. The project shows the feasibility of customizing the framework to support real-world applications, e.g., in health and behavioral domains. Furthermore, to facilitate adoption in a wide range of domains, the project estimates the utility loss via statistical interactions in the data and develops computationally efficient techniques to solve large scale problems. Moreover, the project studies domain-specific privacy risks, e.g., in rapidly growing applications, to formulate the evolving privacy needs in the framework. The results of the project will benefit research studies and applications in a variety of domains that rely on individually contributed data, such as, behavioral and health studies. The project also integrates research and education with a number of interconnected activities, including curriculum development, student mentoring, interdisciplinary collaboration, and K-12 outreach.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.