Every day, massive amounts of data are gathered, exchanged, and used to run statistical computations, train machine learning algorithms, and inform decisions on individuals and populations. The quick rise of data, the need to exchange and process it, to take data privacy concerns into account, and to understand how it affects decision-making, introduce many new and interesting economic, game theoretic, and algorithmic challenges.
The goal of this thesis is to provide theoretical foundations to approach these challenges. The first part of this thesis focuses on the design of mechanisms that purchase then aggregate data from many sources, in order to perform statistical tasks. The second part of this thesis revolves around the societal concerns associated with the use of individuals' data. The first such concern we examine is that of privacy, when using sensitive data about individuals in statistical computations; we focus our attention on how privacy constraints interact with the task of designing mechanisms for acquisition and aggregation of sensitive data. The second concern we focus on is that of fairness in decision-making: we aim to provide tools to society that help prevent discrimination against individuals and populations based on sensitive attributes in their data, when making important decisions about them. Finally, we end this thesis on a study of the interactions between data and strategic behavior. There, we see data as a source of information that informs and affects agents' incentives; we study how information revelation impacts agent behavior in auctions, and in turn how a seller should design auctions that take such information revelation into account.</p
