The advancement of data communication technologies promotes widespread data collection
and transmission in various application domains, thereby expanding big data significantly …

The vast majority of artificial intelligence solutions are founded on game theory, and
differential privacy is emerging as perhaps the most rigorous and widely adopted privacy …

The problem of privacy-preserving data analysis has a long history spanning multiple
disciplines. As electronic data about individuals becomes increasingly detailed, and as …

Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

The study of approximate mechanism design for facility location problems has been in the
center of research at the intersection of artificial intelligence and economics for the last …

We initiate the study of markets for private data, through the lens of differential privacy.
Although the purchase and sale of private data has already begun on a large scale, a theory …

Federated learning is a promising distributed machine learning paradigm that has been
playing a significant role in providing privacy-preserving learning solutions. However …

The literature on algorithmic mechanism design is mostly concerned with game-theoretic
versions of optimization problems to which standard economic money-based mechanisms …

We study the problem of implementing equilibria of complete information games in settings
of incomplete information, and address this problem using" recommender mechanisms." A …

Corporations, organizations, and governments have collected, digitized, and stored
information in digital forms since the invention of computers, and the speed of such data …