49,725 research outputs found
Budget Feasible Mechanism Design: From Prior-Free to Bayesian
Budget feasible mechanism design studies procurement combinatorial auctions
where the sellers have private costs to produce items, and the
buyer(auctioneer) aims to maximize a social valuation function on subsets of
items, under the budget constraint on the total payment. One of the most
important questions in the field is "which valuation domains admit truthful
budget feasible mechanisms with `small' approximations (compared to the social
optimum)?" Singer showed that additive and submodular functions have such
constant approximations. Recently, Dobzinski, Papadimitriou, and Singer gave an
O(log^2 n)-approximation mechanism for subadditive functions; they also
remarked that: "A fundamental question is whether, regardless of computational
constraints, a constant-factor budget feasible mechanism exists for subadditive
functions."
We address this question from two viewpoints: prior-free worst case analysis
and Bayesian analysis. For the prior-free framework, we use an LP that
describes the fractional cover of the valuation function; it is also connected
to the concept of approximate core in cooperative game theory. We provide an
O(I)-approximation mechanism for subadditive functions, via the worst case
integrality gap I of LP. This implies an O(log n)-approximation for subadditive
valuations, O(1)-approximation for XOS valuations, and for valuations with a
constant I. XOS valuations are an important class of functions that lie between
submodular and subadditive classes. We give another polynomial time O(log
n/loglog n) sub-logarithmic approximation mechanism for subadditive valuations.
For the Bayesian framework, we provide a constant approximation mechanism for
all subadditive functions, using the above prior-free mechanism for XOS
valuations as a subroutine. Our mechanism allows correlations in the
distribution of private information and is universally truthful.Comment: to appear in STOC 201
Budget-Feasible Mechanism Design for Non-Monotone Submodular Objectives: Offline and Online
The framework of budget-feasible mechanism design studies procurement
auctions where the auctioneer (buyer) aims to maximize his valuation function
subject to a hard budget constraint. We study the problem of designing truthful
mechanisms that have good approximation guarantees and never pay the
participating agents (sellers) more than the budget. We focus on the case of
general (non-monotone) submodular valuation functions and derive the first
truthful, budget-feasible and -approximate mechanisms that run in
polynomial time in the value query model, for both offline and online auctions.
Prior to our work, the only -approximation mechanism known for
non-monotone submodular objectives required an exponential number of value
queries.
At the heart of our approach lies a novel greedy algorithm for non-monotone
submodular maximization under a knapsack constraint. Our algorithm builds two
candidate solutions simultaneously (to achieve a good approximation), yet
ensures that agents cannot jump from one solution to the other (to implicitly
enforce truthfulness). Ours is the first mechanism for the problem
where---crucially---the agents are not ordered with respect to their marginal
value per cost. This allows us to appropriately adapt these ideas to the online
setting as well.
To further illustrate the applicability of our approach, we also consider the
case where additional feasibility constraints are present. We obtain
-approximation mechanisms for both monotone and non-monotone submodular
objectives, when the feasible solutions are independent sets of a -system.
With the exception of additive valuation functions, no mechanisms were known
for this setting prior to our work. Finally, we provide lower bounds suggesting
that, when one cares about non-trivial approximation guarantees in polynomial
time, our results are asymptotically best possible.Comment: Accepted to EC 201
Bayesian Incentive Compatibility via Fractional Assignments
Very recently, Hartline and Lucier studied single-parameter mechanism design
problems in the Bayesian setting. They proposed a black-box reduction that
converted Bayesian approximation algorithms into Bayesian-Incentive-Compatible
(BIC) mechanisms while preserving social welfare. It remains a major open
question if one can find similar reduction in the more important
multi-parameter setting. In this paper, we give positive answer to this
question when the prior distribution has finite and small support. We propose a
black-box reduction for designing BIC multi-parameter mechanisms. The reduction
converts any algorithm into an eps-BIC mechanism with only marginal loss in
social welfare. As a result, for combinatorial auctions with sub-additive
agents we get an eps-BIC mechanism that achieves constant approximation.Comment: 22 pages, 1 figur
Privacy-Preserving Adversarial Networks
We propose a data-driven framework for optimizing privacy-preserving data
release mechanisms to attain the information-theoretically optimal tradeoff
between minimizing distortion of useful data and concealing specific sensitive
information. Our approach employs adversarially-trained neural networks to
implement randomized mechanisms and to perform a variational approximation of
mutual information privacy. We validate our Privacy-Preserving Adversarial
Networks (PPAN) framework via proof-of-concept experiments on discrete and
continuous synthetic data, as well as the MNIST handwritten digits dataset. For
synthetic data, our model-agnostic PPAN approach achieves tradeoff points very
close to the optimal tradeoffs that are analytically-derived from model
knowledge. In experiments with the MNIST data, we visually demonstrate a
learned tradeoff between minimizing the pixel-level distortion versus
concealing the written digit.Comment: 16 page
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
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