Many online platforms use economic mechanisms to estimate the best ways to match consumers and businesses with products and services. Effective matches may require using personal consumer data but doing so may intrude on consumers' privacy. This project will use formal concepts of privacy to analyze the use of personal information in mechanism design. The goal is to develop tools for understanding the value and cost of collecting and using personal data, and provide mechanisms that allow designers to build systems that make meaningful and well understood tradeoffs between utility and privacy.
The project combines research on mechanism design and econometrics to provide a new perspective on privacy. The project will develop methods that use ideas from econometrics to reveal concrete privacy preferences for individuals and aggregate distributions, and connect those preferences to formal privacy models, including differential privacy. The revealed privacy preferences for individuals, or aggregate for distributions, can then be used to design mechanisms with concrete and meaningful privacy and utility tradeoffs based on users' individual privacy preferences. The broader goal is to transform abstract privacy guarantees into concrete tools for incorporating privacy preferences to maximize consumer utility as well as business decisions.
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.