Today several students will endure the first (3 hour!) lecture in an experimental course I am teaching on privacy and mechanism design. Its a very new area -- whether or not there is enough material to cover a whole semester may depend on how many new theorems are proven during the semester that the course is running. (I already know of the existence of several papers I will want to teach, but haven't read yet since they aren't yet available!)
A secondary goal that I have is to produce a nice set of lecture notes, that will be accessible to people curious about getting into the field. I encourage people to read along, starting with today's Lecture 1. We're starting from basics -- I don't want to assume that readers know anything about either privacy, or mechanism design. In the first lecture, we introduce differential privacy from the perspective of a game theorist, and use it to solve an interesting problem: prior-free revenue maximization in a digital goods auction. Along the way we learn about a useful tool, "The exponential mechanism", that we will encounter again.
The digital goods auction setting is not only a nice introduction to many of the ideas underlying the privacy/mechanism design connection, it is also historically faithful: all of the results in Lecture 1 are from the seminal paper of McSherry and Talwar which introduced the connection between differential privacy and game theory: Mechanism Design via Differential Privacy.
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