Instructor: Aaron Roth
Time: Friday 12:00-3:00 pm
Room: Towne 311
: Differential Privacy
is the name of a recently developed set of tools and goals for
controlling the sensitivity of an algorithm to small changes in its
input. In this seminar, we will consider an exciting set of connections
forged in the last couple of years between this area and game theory
and mechanism design. In general, this connection has two facets:
from differential privacy can be used to design new game theoretic
mechanisms, especially in large markets and games. This is because the
notion of algorithmic sensitivity controlled by differential privacy is
with respect to unilateral player deviations, which is the same notion
of sensitivity required by game-theoretic equilibirum concepts.
Applications here range from designing exactly truthful mechanisms
without the use of money, to designing equilibrium selection mechanisms
which can implement ex-post equilibria in games of incomplete
information, to implementing Vickrey auctions with ascending price
implementations, where truthful bidding remains a dominant strategy
(rather than just a Nash equilibrium).
- Definitions and tools from differential privacy
can be used to model and control agents preferences for privacy, which
are increasingly important. This allows us to study the problem of
whether we can design mechanisms that are truthful even in the presence
for preferences for privacy. Note that this kind of preference is
different from the standard utility commonly studied in mechanism
design, since it involves preference over the mechanism, not just over
the selected outcome. We will also study how to design markets to
procure private data itself.
For a survey of the area, see Pai and Roth
This will be a
mathematically rigorous theory
course, but the only prerequisite is mathematical maturity. Prior
coursework in algorithms, game theory, and mechanism design are helpful, but not required: this class will be self-contained.
Goals and Grading
The goal of
this course is to introduce students to differential privacy and its connections to mechanism design, and then
bring them up to the frontier of modern research. At the end of
this course, students will be able to contribute to the research
literature. As such, the main graded
component of this course will be a research project. This
project can be either a work of pure theory, or it can have a practical
will meet with the instructor over the course of
the semester, present their work at the end of the class, and will be
encouraged to produce a paper with the intention of publishing it.
: New (8/2014)
We now have a book publically available that serves as an introduction to the techniques of differential privacy. Its contents are largely orthogonal to this class, but provide a very useful supplement, and cover the algorithmic tools that would be necessary to carry out research in this area. The Algorithmic Foundations of Differential Privacy
Privacy as a Tool in Mechanism Design and Game Theory
- Definition and motivation of Differential Privacy. Definitions from game theory and mechanism design.
- Basic building blocks: numeric-valued functions, and perturbations from the Laplace distribution
- Basic building blocks: the exponential mechanism and non-numeric valued functions
- Composition theorems for differentially private algorithms
Privacy as a desiderata in Mechanism Design
- Asymptotically truthful mechanisms for digital goods auctions.
- Designing exactly truthful mechanisms without money
- Joint differential privacy and equilibrium selection in games of incomplete information
- Joint differential privacy and auction design
- An Anti-folk theorem in repeated games with imperfect monitoring
Other Topics as time permits (e.g. other approaches to privacy in economics)
- Making the VCG mechanism private
- Designing truthful mechanisms in the presence of privacy preferences
- Designing procurement auctions to gather private data
: See here
for project ideas and timeline.
- A survey on Privacy and Mechanism Design. Pai and Roth 2013.
- A survey on Privacy and Data Based Research. Heffetz and Ligett, 2013.
- Mechanism Design via Differential Privacy. McSherry and Talwar, 2007.
- Approximately Optimal Mechanism Design via Differential Privacy. Nissim, Smorodinsky, and Tennenholtz, 2011.
- Selling Privacy at Auction. Ghosh and Roth, 2011.
- Is Privacy Compatible with Truthfulness? Xiao, 2012.
- Truthful Mechanisms for Agents that Value Privacy. Chen, Cong, Kash, Moran, and Vadhan, 2012.
- Privacy Aware Mechanism Design. Nissim, Orlandi, and Smorodinsky, 2012.
- The Exponential Mechanism for Social Welfare: Private, Truthful, and Nearly Optimal. Huang and Kannan, 2012.
- A Theory of Pricing Private Data. Li, Li, Miklau, and Suciu, 2012.
- Mechanism Design in Large Games: Incentives and Privacy. Kearns, Pai, Roth, and Ullman, 2013.
- Privacy and Coordination: Computing on Databases with Endogenous Participation. Ghosh and Ligett, 2013.
- Linear Regression as a Non-Cooperative Game. Ioannidis and Loiseau, 2013.
- Private Matchings and Allocations. Hsu, Huang, Roth, Roughgarden, and Wu, 2013.
- Cryptography and the Economics of Supervisory Information. Flood, Katz, Ong, and Smith. 2013.
- Asymptotically Truthful Equilibrium Selection in Large Congestion Games. Rogers and Roth, 2013.
- Redrawing the Boundaries on Purchasing Data from Privacy-Sensitive Individuals. Nissim, Vadhan, and Xiao, 2014.
- The Empirical Implications of Privacy-Aware Choice. Cummings, Echenique, and Wierman, 2014.
- An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring. Pai, Roth, and Ullman, 2014.
- Privacy-Preserving Public Information for Sequential Games. Blum, Morgenstern, Sharma, and Smith, 2014.