27,896 research outputs found
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)
We present a mechanism for computing asymptotically stable school optimal
matchings, while guaranteeing that it is an asymptotic dominant strategy for
every student to report their true preferences to the mechanism. Our main tool
in this endeavor is differential privacy: we give an algorithm that coordinates
a stable matching using differentially private signals, which lead to our
truthfulness guarantee. This is the first setting in which it is known how to
achieve nontrivial truthfulness guarantees for students when computing school
optimal matchings, assuming worst- case preferences (for schools and students)
in large markets
Truthful Mechanisms for Agents that Value Privacy
Recent work has constructed economic mechanisms that are both truthful and
differentially private. In these mechanisms, privacy is treated separately from
the truthfulness; it is not incorporated in players' utility functions (and
doing so has been shown to lead to non-truthfulness in some cases). In this
work, we propose a new, general way of modelling privacy in players' utility
functions. Specifically, we only assume that if an outcome has the property
that any report of player would have led to with approximately the same
probability, then has small privacy cost to player . We give three
mechanisms that are truthful with respect to our modelling of privacy: for an
election between two candidates, for a discrete version of the facility
location problem, and for a general social choice problem with discrete
utilities (via a VCG-like mechanism). As the number of players increases,
the social welfare achieved by our mechanisms approaches optimal (as a fraction
of )
Selling Privacy at Auction
We initiate the study of markets for private data, though the lens of
differential privacy. Although the purchase and sale of private data has
already begun on a large scale, a theory of privacy as a commodity is missing.
In this paper, we propose to build such a theory. Specifically, we consider a
setting in which a data analyst wishes to buy information from a population
from which he can estimate some statistic. The analyst wishes to obtain an
accurate estimate cheaply. On the other hand, the owners of the private data
experience some cost for their loss of privacy, and must be compensated for
this loss. Agents are selfish, and wish to maximize their profit, so our goal
is to design truthful mechanisms. Our main result is that such auctions can
naturally be viewed and optimally solved as variants of multi-unit procurement
auctions. Based on this result, we derive auctions for two natural settings
which are optimal up to small constant factors:
1. In the setting in which the data analyst has a fixed accuracy goal, we
show that an application of the classic Vickrey auction achieves the analyst's
accuracy goal while minimizing his total payment.
2. In the setting in which the data analyst has a fixed budget, we give a
mechanism which maximizes the accuracy of the resulting estimate while
guaranteeing that the resulting sum payments do not exceed the analysts budget.
In both cases, our comparison class is the set of envy-free mechanisms, which
correspond to the natural class of fixed-price mechanisms in our setting.
In both of these results, we ignore the privacy cost due to possible
correlations between an individuals private data and his valuation for privacy
itself. We then show that generically, no individually rational mechanism can
compensate individuals for the privacy loss incurred due to their reported
valuations for privacy.Comment: Extended Abstract appeared in the proceedings of EC 201
Privacy and Truthful Equilibrium Selection for Aggregative Games
We study a very general class of games --- multi-dimensional aggregative
games --- which in particular generalize both anonymous games and weighted
congestion games. For any such game that is also large, we solve the
equilibrium selection problem in a strong sense. In particular, we give an
efficient weak mediator: a mechanism which has only the power to listen to
reported types and provide non-binding suggested actions, such that (a) it is
an asymptotic Nash equilibrium for every player to truthfully report their type
to the mediator, and then follow its suggested action; and (b) that when
players do so, they end up coordinating on a particular asymptotic pure
strategy Nash equilibrium of the induced complete information game. In fact,
truthful reporting is an ex-post Nash equilibrium of the mediated game, so our
solution applies even in settings of incomplete information, and even when
player types are arbitrary or worst-case (i.e. not drawn from a common prior).
We achieve this by giving an efficient differentially private algorithm for
computing a Nash equilibrium in such games. The rates of convergence to
equilibrium in all of our results are inverse polynomial in the number of
players . We also apply our main results to a multi-dimensional market game.
Our results can be viewed as giving, for a rich class of games, a more robust
version of the Revelation Principle, in that we work with weaker informational
assumptions (no common prior), yet provide a stronger solution concept (ex-post
Nash versus Bayes Nash equilibrium). In comparison to previous work, our main
conceptual contribution is showing that weak mediators are a game theoretic
object that exist in a wide variety of games -- previously, they were only
known to exist in traffic routing games
- …