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61
Mechanism design via differential privacy
 Proceedings of the 48th Annual Symposium on Foundations of Computer Science
, 2007
"... We study the role that privacypreserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Specifically, we show that the recent notion of differen ..."
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Cited by 212 (3 self)
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We study the role that privacypreserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Specifically, we show that the recent notion of differential privacy [15, 14], in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie. More precisely, mechanisms with differential privacy are approximate dominant strategy under arbitrary player utility functions, are automatically resilient to coalitions, and easily allow repeatability. We study several special cases of the unlimited supply auction problem, providing new results for digital goods auctions, attribute auctions, and auctions with arbitrary structural constraints on the prices. As an important prelude to developing a privacypreserving auction mechanism, we introduce and study a generalization of previous privacy work that accommodates the high sensitivity of the auction setting, where a single participant may dramatically alter the optimal fixed price, and a slight change in the offered price may take the revenue from optimal to zero. 1
Truthful and NearOptimal Mechanism Design via Linear Programming
"... We give a general technique to obtain approximation mechanisms that are truthful in expectation.We show that for packing domains, any ffapproximation algorithm that also bounds the integrality gapof the LP relaxation of the problem by ff can be used to construct an ffapproximation mechanismthat is ..."
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Cited by 134 (12 self)
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We give a general technique to obtain approximation mechanisms that are truthful in expectation.We show that for packing domains, any ffapproximation algorithm that also bounds the integrality gapof the LP relaxation of the problem by ff can be used to construct an ffapproximation mechanismthat is truthful in expectation. This immediately yields a variety of new and significantly improved results for various problem domains and furthermore, yields truthful (in expectation) mechanisms withguarantees that match the best known approximation guarantees when truthfulness is not required. In particular, we obtain the first truthful mechanisms with approximation guarantees for a variety of multiparameter domains. We obtain truthful (in expectation) mechanisms achieving approximation guarantees of O( p m) for combinatorial auctions (CAs), (1 + ffl) for multiunit CAs with B = \Omega (log m) copies ofeach item, and 2 for multiparameter knapsack problems (multiunit auctions). Our construction is based on considering an LP relaxation of the problem and using the classicVCG [25, 9, 12] mechanism to obtain a truthful mechanism in this fractional domain. We argue that the (fractional) optimal solution scaled down by ff, where ff is the integrality gap of the problem, canbe represented as a convex combination of integer solutions, and by viewing this convex combination as specifying a probability distribution over integer solutions, we get a randomized, truthful in expectationmechanism. Our construction can be seen as a way of exploiting VCG in a computational tractable way even when the underlying socialwelfare maximization problem is NPhard.
eBay in the sky: Strategyproof wireless spectrum auctions
 In Proc. of MobiCom
, 2008
"... Marketdriven dynamic spectrum auctions can drastically improve the spectrum availability for wireless networks struggling to obtain additional spectrum. However, they face significant challenges due to the fear of market manipulation. A truthful or strategyproof spectrum auction eliminates the fea ..."
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Cited by 99 (7 self)
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Marketdriven dynamic spectrum auctions can drastically improve the spectrum availability for wireless networks struggling to obtain additional spectrum. However, they face significant challenges due to the fear of market manipulation. A truthful or strategyproof spectrum auction eliminates the fear by enforcing players to bid their true valuations of the spectrum. Hence bidders can avoid the expensive overhead of strategizing over others and the auctioneer can maximize its revenue by assigning spectrum to bidders who value it the most. Conventional truthful designs, however, either fail or become computationally intractable when applied to spectrum auctions. In this paper, we propose VERITAS, a truthful and computationallyefficient spectrum auction to support an eBaylike dynamic spectrum market. VERITAS makes an important contribution of maintaining truthfulness while maximizing spectrum utilization. We show analytically that VERITAS is truthful, efficient, and has a polynomial complexity of O(n 3 k) when n bidders compete for k spectrum bands. Simulation results show that VERITAS outperforms the extensions of conventional truthful designs by up to 200 % in spectrum utilization. Finally, VERITAS supports diverse bidding formats and enables the auctioneer to reconfigure allocations for multiple market objectives.
Approximation Techniques for Utilitarian Mechanism Design
, 2005
"... This paper deals with the design of efficiently computable incentive compatible, or truthful, mechanisms for combinatorial optimization problems with multiparameter agents. We focus on approximation algorithms for NPhard mechanism design problems. These algorithms need to satisfy certain monotonic ..."
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Cited by 92 (5 self)
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This paper deals with the design of efficiently computable incentive compatible, or truthful, mechanisms for combinatorial optimization problems with multiparameter agents. We focus on approximation algorithms for NPhard mechanism design problems. These algorithms need to satisfy certain monotonicity properties to ensure truthfulness. Since most of the known approximation techniques do not fulfill these properties, we study alternative techniques. Our first contribution is a quite general method to transform a pseudopolynomial algorithm into a monotone FPTAS. This can be applied to various problems like, e.g., knapsack, constrained shortest path, or job scheduling with deadlines. For example, the monotone FPTAS for the knapsack problem gives a very efficient, truthful mechanism for singleminded multiunit auctions. The best previous result for such auctions was a 2approximation. In addition, we present a monotone PTAS for the generalized assignment problem with any bounded number of parameters per agent. The most efficient way to solve packing integer programs (PIPs) is LPbased randomized rounding, which also is in general not monotone. We show that primaldual greedy algorithms achieve almost the same approximation ratios for PIPs as randomized rounding. The advantage is that these algorithms are inherently monotone. This way, we can significantly improve the approximation ratios of truthful mechanisms for various fundamental mechanism design problems like singleminded combinatorial auctions (CAs), unsplittable flow routing and multicast routing. Our approximation algorithms can also be used for the winner determination in CAs with general bidders specifying their bids through an oracle.
Mirage: A Microeconomic Resource Allocation System for Sensornet Testbeds
 In Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors
, 2005
"... technical challenges of wireless SensorNets. As the size and demand for these testbeds grow, resource management will become increasingly important to the effectiveness of these environments. In this paper, we argue that a microeconomic resource allocation scheme, specifically the combinatorial auct ..."
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Cited by 78 (7 self)
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technical challenges of wireless SensorNets. As the size and demand for these testbeds grow, resource management will become increasingly important to the effectiveness of these environments. In this paper, we argue that a microeconomic resource allocation scheme, specifically the combinatorial auction, is well suited to testbed resource management. To demonstrate this, we present the Mirage resource allocation system. In Mirage, testbed resources are allocated using a repeated combinatorial auction within a closed virtual currency environment. Users compete for testbed resources by submitting bids which specify resource combinations of interest in space/time (e.g., "any 32 MICA2 motes for 8 hours anytime in the next three days") along with a maximum value amount the user is willing to pay. A combinatorial auction is then periodically run to determine the winning bids based on supply and demand while maximizing aggregate utility delivered to users. We have implemented a fully functional and secure prototype of Mirage and have been operating it in daily use for approximately two months on Intel Research Berkeley's 148mote SensorNet testbed.
Combinatorial auctions for supply chain formation
 in: Second ACM Conference on Electronic Commerce, 2000
"... Supply chain formation presents difficult coordination issues for distributed negotiation protocols. Agents must simultaneously negotiate production relationships at multiple levels, with important interdependencies among inputs and outputs at each level. Combinatorial auctions address this problem ..."
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Cited by 62 (2 self)
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Supply chain formation presents difficult coordination issues for distributed negotiation protocols. Agents must simultaneously negotiate production relationships at multiple levels, with important interdependencies among inputs and outputs at each level. Combinatorial auctions address this problem by global optimization over expressed offers to engage in compound exchanges. Optimizing with respect to offers results in optimal allocations if the offers reflect true values and costs. But autonomous selfinterested agents have an incentive to bid strategically in an attempt to gain
Computational Criticisms of the Revelation Principle
, 2003
"... The revelation principle is a cornerstone tool in mechanism design. It states that one can restrict attention, without loss in the designer's objective, to mechanisms in which A) the agents report their types completely in a single step up front, and B) the agents are motivated to be truthful. ..."
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Cited by 45 (11 self)
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The revelation principle is a cornerstone tool in mechanism design. It states that one can restrict attention, without loss in the designer's objective, to mechanisms in which A) the agents report their types completely in a single step up front, and B) the agents are motivated to be truthful. We show that reasonable constraints on computation and communication can invalidate the revelation principle. Regarding A, we show that by moving to multistep mechanisms, one can reduce exponential communication and computation to linearthereby answering a recognized important open question in mechanism design. Regarding B, we criticize the focus on truthful mechanismsa dogma that has, to our knowledge, never been criticized before. First, we study settings where the optimal truthful mechanism is complete to execute for the center. In that setting we show that by moving to insincere mechanisms, one can shift the burden of having to solve the complete problem from the center to one of the agents. Second, we study a new oracle model that captures the setting where utility values can be hard to compute even when all the pertinent information is availablea situation that occurs in many practical applications. In this model we show that by moving to insincere mechanisms, one can shift the burden of having to ask the oracle an exponential number of costly queries from the center to one of the agents. In both cases the insincere mechanism is equally good as the optimal truthful mechanism in the presence of unlimited computation. More interestingly, whereas being unable to carry out either difficult task would have hurt the center in achieving his objective in the truthful setting, if the agent is unable to carry out either difficult task, the value of the center's objec...
Strategyproof computing: Systems infrastructures for selfinterested parties
 In Workshop on Economics of PeertoPeer Systems
, 2003
"... (Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. ..."
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Cited by 39 (8 self)
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(Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.
Mechanisms for MultiUnit Auctions
 IN PROCEEDINGS OF THE ACM CONFERENCE ON ELECTRONIC COMMERCE (EC
, 2007
"... We present an incentivecompatible polynomialtime approximation scheme for multiunit auctions with general kminded player valuations. The mechanism fully optimizes over an appropriately chosen subrange of possible allocations and then uses VCG payments over this subrange. We show that obtaining ..."
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Cited by 37 (3 self)
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We present an incentivecompatible polynomialtime approximation scheme for multiunit auctions with general kminded player valuations. The mechanism fully optimizes over an appropriately chosen subrange of possible allocations and then uses VCG payments over this subrange. We show that obtaining a fully polynomialtime incentivecompatible approximation scheme, at least using VCG payments, is NPhard. For the case of valuations given by black boxes, we give a polynomialtime incentivecompatible 2approximation mechanism and show that no better is possible, at least using VCG payments.
Choosing Samples to Compute HeuristicStrategy Nash
 In Fifth Workshop on AgentMediated Electronic Commerce
, 2003
"... Auctions define games of incomplete information for which it is often too hard to compute the exact BayesianNash equilibrium. Instead, the infinite strategy space is often populated with heuristic strategies, such as myopic bestresponse to prices. Given these heuristic strategies, it can be usefu ..."
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Cited by 34 (0 self)
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Auctions define games of incomplete information for which it is often too hard to compute the exact BayesianNash equilibrium. Instead, the infinite strategy space is often populated with heuristic strategies, such as myopic bestresponse to prices. Given these heuristic strategies, it can be useful to evaluate the strategies and the auction design by computing a Nash equilibrium across the restricted strategy space. First, it is necessary to compute the expected payoff for each heuristic strategy profile. This step involves sampling the auction and averaging over multiple simulations, and its cost can dominate the cost of computing the equilibrium given a payoff matrix. In this paper, we propose two information theoretic approaches to determine the next sample through an interleaving of equilibrium calculations and payoff refinement. Initial experiments demonstrate that both methods reduce error in the computed Nash equilibrium as samples are performed at faster rates than naive uniform sampling. The second, faster method, has a lower metadeliberation cost and better scaling properties. We discuss how our sampling methodology could be used within experimental mechanism design.