Fair Value of Data and Privacy in Distributed Learning


With the ubiquity of data-driven decision making, understanding the true value of data is critical. This project marries the economic study of data with the growing field of data privacy to present a framework for quantifying the value of data and privacy. In particular, we emphasize the concept of fair payment for data, progressing our understanding of what constitutes an equitable and open data market.

To construct this framework, we draw from many disciplines including game theory, optimization, machine learning, probability theory, and statistics. We aim to bridge the recent advancements in rigorous privacy guarantees for statistical inference and machine learning settings with the economics of quantifying the value of data under heterogeneous privacy requirements.