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 becoming a gold standard notion of privacy; it offers a guaranteed
bound on loss of privacy due to release of query results, even under worst-case …

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 …

Statistical agencies face a dual mandate to publish accurate statistics while protecting
respondent privacy. Increasing privacy protection requires decreased accuracy …

Personal data has value to both its owner and to institutions who would like to analyze it.
Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of …

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

With the rapid growth of smartphones, mobile crowdsensing emerges as a new paradigm
which takes advantage of the pervasive sensor-embedded smartphones to collect data …

We consider a scenario in which a database stores sensitive data of users and an analyst
wants to estimate statistics of the data. The users may suffer a cost when their data are used …

What can we, as users of microdata, formally guarantee to the individuals (or firms) in our
dataset, regarding their privacy? We retell a few stories, well-known in data-privacy circles …