Keep Your Promise: Mechanism Design against Free-riding and False-reporting in Crowdsourcing

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1 Keep Your Promise: Mechanism Design against Free-riding and False-reporting in Crowdsourcing Xiang Zhang, Student Member, IEEE, Guoliang Xue, Fellow, IEEE, uozhou Yu, Student Member, IEEE, Dejun Yang, Member, IEEE, and Jian Tang, Senior Member, IEEE Abstract Crowdsourcing is an emerging paradigm where users can have their tasks completed by paying fees, or receive rewards for providing service. A critical problem that arises in current crowdsourcing mechanisms is how to ensure that users pay or receive what they deserve. Free-riding and false-reporting may make the system vulnerable to dishonest users. In this paper, we design schemes to tackle these problems, so that each individual in the system is better off being honest and each provider prefers completing the assigned task. We first design a mechanism EFF which eliminates dishonest behavior with the help from a trusted third party for arbitration. We then design another mechanism DFF which, without the help from any third party, discourages dishonest behavior. We prove that EFF eliminates free-riding and false-reporting, while guaranteeing truthfulness, transaction-wise budget-balance, and computational efficiency. We also prove that DFF is semi-truthful, which discourages dishonest behavior such as free-riding and false-reporting when the rest of the individuals are honest, while guaranteeing transaction-wise budget-balance and computational efficiency. Performance evaluation shows that within our mechanisms, no user could have a utility gain by unilaterally being dishonest. Index Terms Crowdsourcing, free-riding, false-reporting, game theory, incentive mechanisms. A. Crowdsourcing 1. INTODUCTION For the past few years, we have witnessed the proliferation of crowdsourcing [16] as it becomes a booming online market for labor and resource redistribution. One example is mobile crowdsourcing [2, 34], which leverages a cloud computing platform for recruiting mobile users to collect data (such as photos, videos, mobile user activities, etc) for applications in various domains such as environmental monitoring, social networking, healthcare, transportation, etc. Several commercial crowdsourcing websites, such as Yelp [1], Yahoo! Answers [2], Amazon Mechanical Turk [3], and UBE [4], provide trading markets where some users (service requesters) can have their tasks completed by paying fees, and some other users (service providers) can receive rewards for providing service. Zhang, Xue, and Yu are with Arizona State University, Tempe, AZ {xzhan229, xue, ruozhouy}@asu.edu. Yang is with Colorado School of Mines, Golden, CO djyang@mines.edu. Tang is with Syracuse University, Syracuse, NY jtang2@syr.edu. This research was supported in part by AO grants W911NF and W911NF and NSF grants , , , , and The information reported here does not reflect the position or the policy of the federal government. This is an extended and enhanced version of the paper [3] that appeared in IEEE GLOBECOM 214. An important functionality of these websites is to empower platforms not only to offer markets for laboring and resource redistribution, but also to help decide fair prices for all providers and requesters. In general, a requester would post a task on one of these platforms, and wait till some provider to work on it. To incentivize providers for their service, the requester needs to offer a certain reward. A provider will get reward as compensation for its cost of completing the task. B. Auction Theory Auction is an efficient mechanism for trading markets, with its advantage in discovering prices for buyers and sellers. Auctions involving the interactions among multiple buyers and multiple sellers are called double auctions [12]. When determining payments for buyers and sellers, it is always feared that the prices are manipulated to make the free market vulnerable to dishonest individuals. Therefore, several economic properties, such as truthfulness, individual rationality, budget-balance, and computational efficiency, are desired. Here are the definitions of these properties: Truthfulness: An auction is truthful if no individual can achieve a higher utility by reporting a value deviating from its true valuation (or cost) regardless of the bids (or asks) from other individuals. Individual ationality: An auction is individually rational if for any buyer or seller, it will not get a negative utility by revealing its true valuation (or cost). Budget-balance: An auction is budget-balanced if the auctioneer always makes a non-negative profit. Computational Efficiency: An auction is computationally efficient if the whole auction process can be conducted within polynomial time. Among these properties, truthfulness is the crowning jewel which incentivizes all participants to be honest during the auction [, 11, 19, 27, 34, 37]. However, this alone cannot guarantee the robustness of the mechanism, since other dishonest behavior, such as free-riding and false-reporting (to be defined later), may make the crowdsourcing system vulnerable. C. Free-riding and False-reporting in Crowdsourcing Truthfulness of an auction can prevent individuals benefit from lying about the prices. However, this alone is not adequate. 1

2 Free-riding and false-reporting can make the mechanism vulnerable to dishonest users. If the payment is made before the provider starts to work, a provider always has the incentive to take the payment and devote no effort to complete the task, which is known as free-riding [36]. If the payment is made after the provider completes its work, the requester always has the incentive to refuse the payment by lying about the status of this task, which is known as false-reporting [36]. Thus, extra precautions are necessary. Most recent works [1, 1, 36] are focused on avoiding free-riding and false-reporting by building up reputation systems or grading systems. These mechanisms may discourage such dishonest behavior overall, but based on the assumption that all individuals are patient and they would stay in the system. However, in reality, some dishonest individuals may stay in the system for a short period of time, and get huge rewards by being dishonest. In addition, impatient users may leave the platform since they feel unsafe and being cheated. In this paper, we tackle free-riding and false-reporting by a game-theoretic approach, such that free-riding and falsereporting are eliminated or discouraged. D. Game Theory Basics Game theory [23] is a field of study where interactions among different players are involved. Each player wants to maximize its own utility, which depends on not only its own strategies, but also other players strategies. We need the concept of dominant strategy and Nash Equilibrium [24] as follows. Dominant Strategy: A strategy s dominates all other strategies if the payoff to s is no less than the payoff to any other strategy, regardless of the strategies chosen by other players. s is a strongly dominant strategy if its payoff is strictly larger than the payoff to any other strategy. Nash Equilibrium (NE): When each player has chosen a strategy and no player can benefit by unilaterally changing its strategy, the current set of strategies constitutes an NE. Extensive-form game theory [23] is an important branch of game theory. An extensive-form game is a game where sequencing players strategies and their choices at each decision point is allowed. The corresponding equilibrium, which is called Sub-game Perfect Equilibrium (SPE), is a strategy profile where in every sub-game, an NE is reached by the corresponding subset of strategies. E. Summary of Contributions In this paper, we design two novel auction-based mechanisms, EFF and DFF, to avoid free-riding and false-reporting, while incentivizing providers to complete their assigned tasks. These mechanisms are based on any existing truthful double auction scheme for winner selection and pricing. Each auction winner is required to submit a warranty first, then submit a report on the status of the corresponding task. Based on these reports from providers and requesters, the final payments are determined by the platform. The main contributions of this paper are the following: To the best of our knowledge, we are the first to solve free-riding and false-reporting in each single round. We design a mechanism EFF, which, with the help of arbitrations, eliminates free-riding and false-reporting, and incentivizes providers to complete their assigned tasks. We also prove that EFF is truthful, transaction-wise budgetbalanced, and computationally efficient. We design a mechanism DFF, which, without any arbitration, discourages individuals from being dishonest. We prove that DFF is semi-truthful, which means that no individual could have a higher utility by lying when others are honest. We also prove that DFF is transactionwise budget-balanced and computationally efficient, while incentivizing providers to finish their assigned tasks. The remainder of this paper is organized as follows. In Section 2, we briefly review the current literature on crowdsourcing mechanisms, truthful auctions, and mechanisms avoiding freeriding and false-reporting. In Section 3, we describe the system model. We present and analyze EFF and DFF in Section 4 and Section, respectively. We present performance evaluation in Section 6 and draw our conclusions in Section ELATED WOK Many incentive mechanisms have been proposed for crowdsourcing and mobile sensing [, 6, 9, 11, 17, 2, 26, 27,, 32, 34, 37]. In 212, Yang et al. [34] studied two models: user-centric and platform-centric models. For the user-centric model, an auction-based mechanism is presented, which is computationally efficient, individually rational, profitable, and truthful. For the platform-centric model, the Stackelberg game is formulated and the unique Stackelberg Equilibrium is calculated to maximize the platform utility. In [9], DiPalantino and Vojnovic proposed an all-pay auction, where each provider may choose the job that it wants to work on. In [17], an online question and answer forum was introduced, where each user has a piece of information and can decide when to answer the question. Singla and Krause in [27] proposed BP-UCB, which is an online truthful auction mechanism achieving nearoptimal utilities for all providers. A crowdsourcing Bayesian auction is proposed in [], where the system is aware of the distribution of valuations of all users. With this assumption, two techniques were applied to the new Bayesian optimal auction with high system performance. Zhao et al. [37] proposed an online truthful auction for crowdsourcing with a constant approximation ratio with respect to the platform utility. In [11], a truthful auction algorithm of task allocation to optimize the total utility of all smartphone users was proposed by Feng et al. for the offline model, and an online model was also introduced which reaches a constant approximation ratio of 1 2. Many other truthful auctions also fit for crowdsourcing and mobile sensing. There are two basic truthful double auctions, VCG [7, 14, 28] and McAfee [21], from which most of the current truthful auctions are derived. Along this line, many 2

3 mechanisms [8, 13, 18, 19, 22, 29, 33] are proposed. While they are not specifically designed for crowdsourcing platforms, they can be applied to the scenario with minor modifications. These auction schemes are based on the assumption that all providers will complete the assigned tasks, and all requesters are satisfied with the status of the tasks. This may not always happen. Therefore, there are works focusing on how to make the mechanism robust against free-riding and falsereporting [1, 1, 36]. In these papers, free-riding and falsereporting are avoided by building reputation systems or grading systems. Such systems are based on the assumption that all individuals are patient and will stay in the system for a very long time. Thus, a user may gain extra benefit by free-riding or false-reporting during a short period of time. Impatient users may feel being cheated and turn to other platforms. 3. SYSTEM MODEL In this section, we present the crowdsourcing model and the corresponding double auction model, and define the utilities for the platform, the providers, and the requesters. There is a set of m requesters = { 1, 2,..., m }. For each i, it requires service to complete its task T i, which has a private valuation v i > to i if T i is completed. T i is tagged with a bid b i by i, which is the maximum reward that i is willing to pay for the completion of T i. Note that b i is not necessarily equal to v i. We define the bid vector b = (b 1, b 2,..., b m ). There is a set of n providers P = {P 1, P 2,..., P n }. Each provider P j has a cost c i j > to complete T i, where c i j is set to + if P j is unable to complete T i. P j would post an ask a i j for T i, which is the minimum amount of reward that P j demands for completing T i. Note that a i j is not necessarily equal to ci j. We define the cost vector c j = (c 1 j, c2 j,..., cm j ), the ask vector a j = (a 1 j, a2 j,..., am j ), and the ask matrix A = (a 1; a 2 ;...; a n ). We assume that providers and requesters are non-colluding. By applying a sealed-bid single-round double auction where requesters are buyers and providers are sellers, we get a set W of winning requester-provider pairs, such that ( i, P j ) W if and only if P j is assigned to complete T i. Each provider is assigned to at most one task, while no more than one provider works on each task. We define the function δ( ) : P such that δ(j) = i if and only if ( i, P j ) W, and δ(j) = if P j is not assigned to any task. As a result of the auction, i will be charged β i, and P j will receive α δ(j) j. As a technical convention, we define β i = if i is not a winning requester, and αj i = if ( i, P j ) / W. We define β = (β 1, β 2,..., β m ), α j = (αj 1, α2 j,..., αm j ), and α = (α 1; α 2 ;...; α n ). The platform (auctioneer) has a profit Σ (i,p j) W(β i αj i ) from the auction. The auction mechanism, denoted as M, takes the bid vector b and the ask matrix A as input. M outputs the winning requesterprovider pair set W and the payments α and β. Thus, we have (W, β, α) M(b, A). We call an auction mechanism M transaction-wise budgetbalanced if β i αj i, for each ( i, P j ) W. Note that transaction-wise budget-balance implies budget-balance, i.e., ( (β i,p j) W i αj i ). Throughout this paper, we assume that M is truthful, individually rational, transaction-wise budget-balanced, and computationally efficient. All mechanisms in [8, 18, 29,, 33] satisfy these properties. In particular, TASC [33] is used in our implementation. For each ( i, P j ) W, the platform collects warranties from both i and P j. We model the post-auction process as an extensive-form game between P j and i. In this game, P j first decides the effort towards completing T i ; then i and P j evaluate the result and submit independent reports on the status of T i to the platform. We assume that P j is capable of completing T i, hence the status of T i depends on the willingness of P j. The reports are either C, which is short for Completed, or I, which is short for Incomplete. Both i and P j know the status of T i. However, the platform is not aware of T i s status. The platform decides the final payment for each individual according to these reports. If i and P j submit the same report, the platform would believe that both of them are telling the truth. Otherwise, the platform concludes that one of them lies and consults for arbitration if available. We assume that there are no ambiguities on the status of T i. The utility of i is defined as U i = x i v i (w i β i ), (3.1) where x i is the indicator of the status of T i : x i = 1 if T i is completed; x i = otherwise. wi is the warranty that i pays to the platform, and β i is the final payment that i receives from the platform after the platform collects the reports. The utility of P j is defined as where y δ(j) j U P j = (ᾱ δ(j) j wj P ) y δ(j) j c δ(j) j, (3.2) is the indicator of P j s devotion on T δ(j) : y δ(j) j = 1 is the warranty if P j worked on T δ(j) ; y δ(j) j = otherwise. w P j from P j, and ᾱ δ(j) j is the final amount that P j receives from the platform after the platform collects the reports. The platform utility is defined as U = ( i,p j) W [(w i β i + (w P j ᾱ δ(j) j ) z i τ i ], (3.3) where z i is the indicator of arbitration on T i : z i = 1 if the platform has consulted for arbitration on T i ; z i = otherwise. τ i is the corresponding arbitration fee. We assume that τ i is a value known by all providers, requesters, and the platform. For each ( i, P j ) W, we define U( i, P j ) = (w i β i ) + (w P j ᾱ δ(j) j ) z i τ i, (3.4) which is the portion of platform utility contributed by ( i, P j ). Clearly, U = ( U( i,p j) W i, P j ). emark 3.1: The utilities of providers, requesters, and the auctioneer in this paper are different from those in [3]. Another difference between this paper and [3] is that we model the post-auction process into an extensive-form game, in contrast to a simultaneous game. The notations of this paper are summarized in Table 1. 3

4 Symbol Meaning TABLE 1 NOTATIONS i, P j provider (seller), requester (buyer), P the set of providers, requesters T i i s task v i valuation of T i b/b i bid vector/ bid from i c j /c i j cost vector of P j / cost of P j to finish T i A/a j /a i j ask matrix/ ask vector of P j / ask of P j to finish T i W set of winning provider-requester pairs δ( ) assignment function from providers to requesters β i payment of i from the auction β payment vector for requesters from the auction α i j payment of P j for T i from the auction α j payment vector of P j from the auction α payment matrix of providers from the auction M the auction mechanism Ui /U j P /U utility of i/ P j / the platform x i indicator of T i s status y δ(j) j indicator of P j s devotion on T δ(j) z i indicator of arbitration on T i τ i arbitration fee of T i β i final payment that i receives from the platform ᾱ δ(j) j final payment that P j receives from the platform 4. EFF: ELIMINATING FEE-IDING AND FALSE-EPOTING WITH ABITATION In this section, we present a crowdsourcing mechanism EFF, which, with the help from a trusted third party for arbitration, eliminates free-riding and false-reporting and incentivizes each provider to complete the assigned task. A. Description of EFF In EFF, a trustworthy third party is available, who can provide arbitration on the status of T i with an arbitration fee τ i >. The first part of EFF applies auction M, where W, β, and α are decided. After the auction, the platform collects a warranty wi = β i + τ i from i and a warranty wj P = θαj i + τ i from P j for each ( i, P j ) W, where θ > is a system parameter. We use θαj i to help incentivize P j to complete T i. If P j has been assigned to work on T i but does not complete T i, it will receive a penalty of θαj i. After i and P j submitted their warranties, an extensiveform game is applied. P j decides whether or not to work on T i. If P j chooses to devote effort on T i, T i would be completed. Otherwise, T i is incomplete with no effort devoted by P j. Then i and P j submit their independent reports on the status of T i. Gathering the reports from i and P j, the platform decides the final payments β i and ᾱj i for i and P j, respectively. The formal description of EFF is presented as Algorithm 1. With the payments β i and ᾱj i from Algorithm 1, utilities of i and P j can be computed based on equations (3.1) and (3.2), respectively. These utility values are shown in Table 2. The first row indicates P j s effort on T i and the second row lists the reports from P j. The first column lists the reports from Algorithm 1: EFF 1 (W, β, α) M(b, A); 2 ( i, P j ) W, w i τ i + β i, w P j τ i + θα i j ; 3 i submits w i and P j submits w P j to the platform; 4 β i w i ; ᾱi j wp j ; P j decides whether or not to work on T i, and devotes the corresponding effort; 6 i and P j submit independent reports on the status of T i to the platform; 7 if eports are different then 8 The platform consults arbitration for the status of T i, and pays τ i to the arbitrator; 9 if i submits a false report then 1 βi β i τ i ; 11 else 12 ᾱ i j ᾱi j τ i; 13 end // The arbitration fee is taken from the liar 14 else 1 The platform adopts the reports from i and P j ; 16 end 17 if The platform concludes that T i is completed then 18 βi β i β i ; ᾱ i j ᾱi j + αi j ; // The auction payment is made 19 else 2 ᾱ i j ᾱi j θαi j ; // θα i j is taken from P j for not completing T i 21 end 22 The platform returns β i to i and ᾱ i j to P j, respectively. i. For each of the 2-tuples, the first element is the utility of i, and the second element is the utility of P j. We pick two entries in Table 2 and explain how the corresponding utilities are derived. First, we explain the entry where T i is completed and both i and P j submit C (marked in red in Table 2). In Line 2 of Algorithm 1, we have wi = τ i + β i and wj P = τ i + θαj i. In Line 4, we have β i = τ i + β i and ᾱj i = τ i+θαj i. Because both i and P j submit C, no arbitration is consulted, and the platform concludes that T i is completed. Thus, βi = (τ i + β i ) β i = τ i. Since P j has devoted effort on T i and T i is completed, we have yj i = 1 and x i = 1. In Line 18, we have ᾱj i = (τ i + θαj i ) + αi j. Based on equations (3.1) and (3.2), i s utility is v i β i and P j s utility is αj i ci j. Next, we explain the entry where T i is incomplete, i submits I, and P j submits C (marked in blue in Table 2). In Line 2, we have wi = τ i + β i and wj P = τ i + θαj i. In Line 4, we have β i = τ i + β i and ᾱj i = τ i + θαj i. Because i and P j submit different reports, an arbitration is consulted and the arbitration result indicates that P j submits the dishonest report. In Line 12, we have ᾱj i = (τ i + θαj i ) τ i = θαj i. Because T i is incomplete, we have yj i =, x i =. In Line 2, we have 4

5 TABLE 2 UTILITIES OF i AND P j IN EFF FO ( i, P j ) W, THE ENTY FO THE UNIQUE SPE IS MAKED BY. i C I C I P j T i completed T i not completed C (v i β i, α i j ci j ) (v i β i, α i j ci j τ i) ( β j, α i j ) ( τ i, θα i j ) I (v i β i τ i, α i j ci j ) (v i, θα i j ci j ) (, θαi j τ i) (, θα i j ) ᾱj i = θαi j θαi j =. Based on equations (3.1) and (3.2), i s utility is and P j s utility is θαj i τ i. According to Table 2, there is a unique SPE. If P j completes T i, P j would report C regardless of what i reports. Since P j reports C, i prefers reporting C to reporting I. Hence, if P j completes T i, both i and P j would report C. For similar reason, if P j does not complete T i, both i and P j would report I. Comparing the utilities of P j when completing T i (which is αj i ci j > ) and not completing T i (which is θαj i < ), P j prefers completing T i. Thus, the unique SPE is reached when P j completes T i, and both i and P j submit C. B. Analysis of EFF In this subsection, we prove several properties of EFF. Theorem 1: EFF eliminates free-riding and false-reporting, while guaranteeing truthfulness, transaction-wise budgetbalance, and computational efficiency. To prove this theorem, we need to prove lemmas ecall that auction M is individually rational, transaction-wise budget-balanced, computationally efficient, and truthful. Lemma 4.1: EFF eliminates free-riding and false-reporting, while guaranteeing truthfulness. Proof: Suppose that ( i, P j ) is in W when i bids v i and P j bids c j. The unique SPE is achieved when P j completes T i and both i and P j submit C. According to Table 2 and the individual rationality of M, i s utility is v i β i and P j s utility is αj i ci j. When i or P j changes its bid during the auction, due to the truthfulness of M, the corresponding payment of i could not be lower than β i and the corresponding payment of P j could not be higher than αj i. Thus, neither i nor P j has incentive to change its bid. When i submits I for false-reporting, P j still reports C since it is P j s dominant strategy when T i is completed. i s corresponding utility would be v i β i τ i < v i β i. When P j chooses not to complete T i but submits C for freeriding, i still reports I since it is i s dominant strategy when T i is incomplete. P j s corresponding utility would be θαj i τ i < αj i ci j. Thus, neither i nor P j could benefit from being dishonest. Suppose that i loses the auction by bidding b i = v i. Its utility is. Suppose that i changes its bid. If i still loses the auction, its utility remains to be. If i wins the auction, its new payment in the auction β i is no smaller than v i since M is truthful. If T i is completed, according to Table 2, i s utility is no more than v i β i v i b i = since P j would always report C. If T i is incomplete, i could not have a positive utility. Thus, i could not benefit from being dishonest. Suppose that P j loses the auction by asking a j = c j. Its utility is. Suppose that P j changes its ask. If P j still loses the auction, its utility remains to be. If P j wins the auction and is assigned to complete T i, P j s new payment in the auction αj i is no larger than ci j since M is truthful. If P j completes T i, according to Table 2, its utility is no larger than αj i ci j a i j ci j =. If P j does not complete T i, its utility is no more than θαj i since i would report I. Thus, P j could not benefit from being dishonest. To sum up all cases, EFF eliminates free-riding and falsereporting, while guaranteeing truthfulness. emark 4.1: By Lemma 4.1, dishonest behaviors are eliminated in EFF. According to the unique SPE in the extensiveform game in EFF, for each ( i, P j ) W, P j would finish T i, which indicates that EFF incentivizes each winning provider to complete its assigned task. Lemma 4.2: EFF is transaction-wise budget-balanced. Proof: Note that by the transaction-wise budget-balance of M, β i αj i for each ( i, P j ) W. We prove this lemma by the following case analysis: If i and P j both submit C, the platform believes that T i is completed without consulting for arbitration. Thus, we have β i = τ i and ᾱj i = τ i + (1 + θ)αj i. By equation (3.4), we have U( i, P j ) = β i αj i. If i and P j both submit I, the platform believes that T i is incomplete without consulting for arbitration. Thus, we have β i = τ i + β i and ᾱj i = τ i. By equation (3.4), we have U( i, P j ) = θαj i. If i submits C and P j submits I when T i is completed, the platform consults for arbitration, and the result shows that P j lies in the report. Thus, we have β i = τ i + β i and ᾱj i = (1 + θ)αi j. By equation (3.4), we have U( i, P j ) = β i αj i. If i submits C and P j submits I when T i is incomplete, the platform consults for arbitration, and the result shows that i lies in the report. Thus, we have β i = β i and ᾱj i = τ i. By equation (3.4), we have U( i, P j ) = θαj i. If i submits I and P j submits C when T i is completed, the platform consults for arbitration, and the result shows that i lies in the report. Thus, we have β i = and ᾱj i = τ i + (1 + θ)αj i. By equation (3.4), we have U( i, P j ) = β i αj i. If i submits I and P j submits C when T i is incomplete, the platform consults for arbitration, and the result shows that P j lies in the report. Thus, we have β i = τ i + β i and ᾱj i =. By equation (3.4), we have U( i, P j ) = θαj i. Thus, EFF is transaction-wise budget-balanced.

6 Lemma 4.3: EFF is computationally efficient. Proof: Since M is computationally efficient, and report submission, arbitration, and pricing all take constant time, EFF is computationally efficient. Lemmas complete the proof for Theorem 1. emark 4.2: EFF is not individually rational. To incentivize each winning provider to complete its assigned task, we impose a penalty θαj i in Algorithm 1, Line 2. If P j fails to complete T i but behaves honestly, its utility is θαj i <, which violates individual rationality. However, this incentivizes P j to complete the task. This part differs from that in [3], where no such penalty is imposed and individual rationality is achieved.. DFF: DISCOUAGING FEE-IDING AND FALSE-EPOTING WITHOUT ABITATION EFF successfully eliminates free-riding and false-reporting with the help from a trusted third party for arbitration. However, it is not true that an arbitration is always available. In this section, we present another mechanism DFF, which, without any arbitration, discourages free-riding and false-reporting, while still guaranteeing several economic properties. A. Description of DFF Same as EFF, the first part of DFF applies the auction M. For each ( i, P j ) W, DFF collects a warranty wi = (1 + ζ)β i from i and a warranty wj P = (ζ + η)αj i from P j, where ζ > and η > are system parameters. In DFF, we use ζ to help discourage dishonest behavior, and η to help incentivize providers to complete their assigned tasks. After the auction, i and P j pay their warranties to the platform. We model the post-auction part of DFF as another extensive-form game. P j first decides whether or not to work on T i, and devotes the corresponding effort. Then both i and P j submit their independent reports on the status of T i. With the reports from i and P j, the platform decides the final payments β i and ᾱj i for each of the following cases: When i and P j both submit C, we have β i = ζβ i and ᾱj i = (ζ + η)αi j + αi j. This means that i pays β i to the platform and P j receives αj i from the platform. When i and P j both submit I, we have β i = (1 + ζ)β i and ᾱj i = ζαi j. This means that i pays nothing to the platform and P j pays ηαj i to the platform. When i submits C and P j submits I, we have β i = ζβ i and ᾱj i = ζαi j. This means that i pays β i to the platform and P j pays ηαj i to the platform. When i submits I and P j submits C, we have β i = and ᾱj i =. This means that i pays (1 + ζ)β i to the platform and P j pays (ζ + η)αj i to the platform. The formal description of DFF is presented as Algorithm 2. Based on the final payments β i and ᾱj i from Algorithm 2, utilities of i and P j can be computed based on equations (3.1) and (3.2), respectively. These utility values are shown in Algorithm 2: DFF 1 (W, β, α) M(b, A); 2 ( i, P j ) W, w i (1 + ζ)β i, w P j (ζ + η)αi j ; 3 i submits w i and P j submits w P j to the platform; 4 β i w i ; ᾱi j wp j ; P j decides whether or not to work on T i, and devotes the corresponding effort; 6 i and P j submit independent reports on the status of T i to the platform; 7 if (Both i and P j submit C) then 8 βi β i β i ; ᾱ i j ᾱi j + αi j ; 9 else if (Both i and P j submit I) then 1 ᾱ i j ᾱi j ηαi j ; 11 else if ( i submits C and P j submits I) then 12 βi β i β i ; ᾱ i j ᾱi j ηαi j ; 13 else 14 βi β i (1 + ζ)β i ; ᾱ i j ᾱi j (ζ + η)αi j ; 1 end 16 The platform returns β i to i and ᾱ i j to P j, respectively. Table 3. Notations in Table 3 represent similar meanings as those in Table 2. We pick two entries and explain how the corresponding utilities are derived. First, we explain the entry where T i is completed and both i and P j submit C (marked in red in Table 3). In Line 2 of Algorithm 2, we have wi = (1 + ζ)β i and wj P = (ζ + η)αj i. In Line 4, we have β i = (1 + ζ)β i and ᾱj i = (ζ + η)αi j. Because both i and P j submit C, in Line 8, we have β i = (1 + ζ)β i β i = ζβ i and ᾱj i = (ζ + η)αi j + αi j. Since P j has devoted effort on T i and T i is completed, we have yj i = 1 and x i = 1. Based on equations (3.1) and (3.2), i s utility is v i β i and P j s utility is αj i ci j. Next, we explain the entry where T i is incomplete, i submits I, and P j submits C (marked in blue in Table 3). In Line 2, we have wi = (1+ζ)β i and wj P = (ζ + η)αj i. In Line 4, we have β i = (1 + ζ)β i and ᾱj i = (ζ + η)αi j. Because i submits I and P j submits C, in Line 14, we have β i = (1 + ζ)β i (1 + ζ)β i = and ᾱj i = (ζ + η)αi j (ζ + η)αi j =. Because T i is incomplete, we have yj i = and x i =. Based on equations (3.1) and (3.2), i s utility is β i ζβ i and P j s utility is ζαj i ηαi j. B. Analysis of DFF In this subsection, we introduce the definition of semitruthfulness and prove several properties of DFF. Definition.1: A mechanism is semi-truthful if each individual has no positive increment in utility when it unilaterally behaves dishonestly while others are being honest. Comparing the definitions of semi-truthfulness and truthfulness, semi-truthfulness requires a stronger requirement, assuming that the other individuals are honest, while truthfulness has no such assumption. Theorem 2: DFF is semi-truthful, transaction-wise budgetbalanced, and computationally efficient. 6

7 TABLE 3 UTILITIES OF i AND P j IN DFF FO EACH ( i, P j ) W, THE ENTY FO EACH EQUILIBIUM IS MAKED BY. i C I C I P j T i completed T i not completed C (v i β i, α i j ci j ) (v i β i, c i j ηαi j ) ( β i, α i j ) ( β i, ηα i j ) I (v i β i ζβ i, c i j ζαi j ηαi j ) (v i, c i j ηαi j ) ( β i ζβ i, ζα i j ηαi j ) (, ηαi j ) To prove Theorem 2, we need the following three lemmas. Lemma.1: DFF is semi-truthful. Proof: Suppose that ( i, P j ) is in W when i bids v i and P j bids c j. If P j completes T i and no individual lies, i s utility is v i β i and P j s utility is αj i ci j. Suppose that i bids a value deviating from v i, it will not pay a lower payment due to the truthfulness of M. If i lies by reporting I instead of C, and P j does not lie and reports C, i s utility would be v i (1+ζ)β i v i (1+ζ)β i v i β i, where β i is the corresponding payment after i changes its bid. Thus, i has no incentive to lie when T i is completed and P j is honest. For P j, by deviating its ask from c j, it will not receive a higher payment. If P j lies by reporting I, and i does not lie and reports C, P j s corresponding utility would be ηα i j c i j α i j c i j αi j ci j, where α i j is the corresponding payment of P j after P j changes its bid. Thus, P j has no incentive to lie when T i is completed and i is honest. If P j chooses not to complete T i and reports I honestly, its utility would be ηα i j <. If P j reports C dishonestly, since i is honest and reports I, P j s utility would be (η + ζ)α i j <. Thus, P j would complete T i. Therefore, if i or P j knows that the other one is honest, it would be honest. Suppose that i loses the auction by bidding b i = v i. Its original utility is. Suppose that i changes its bid. If it still loses the auction, its utility remains to be. If it wins the auction and P j is assigned to T i, i s payment in the auction β i is no smaller than v i since M is truthful. If T i is completed, according to Table 2, i s utility is no larger than v i β i v i b i = since P j is honest and reports C. If T i is incomplete, i could not have a positive utility. Thus, i could not benefit from unilaterally being dishonest. Suppose that P j loses the auction by asking a j = c j. Its original utility is. Suppose that P j changes its ask. If it still loses the auction, its utility remains to be. If it wins the auction and is assigned to T i, P j s payment in the auction αj i is no larger than c i j since M is truthful. If P j completes T i, according to Table 2, its utility is no larger than αj i ci j a i j ci j =. If P j does not complete T i, it utility is no more than θαj i since i is honest and reports I. Thus, P j could not benefit from unilaterally being dishonest. To sum up all cases, DFF is semi-truthful emark.1: With semi-truthfulness, we know that there is one equilibrium left in correspondence with each status of T i. If P j completes T i, both i and P j would report C. If P j does not complete T i, both i and j would report I. Comparing these two equilibria, P j has a utility of αj i ci j > in the first equilibrium, and a utility of ηαj i < in the second one. Thus, P j prefers to complete T i, which makes the first equilibrium the unique SPE and encourages all winning providers to complete their assigned tasks, while discouraging free-riding and falsereporting. Note that in [3], the simultaneous game computes two equilibria with no preference of P j to complete T i or not. However, by applying the extensive-form game instead of the simultaneous game, we have one unique SPE which incentivizes all winning providers to complete the tasks and discourages dishonest behaviors. Lemma.2: DFF is transaction-wise budget-balanced. Proof: Since M is transaction-wise budget-balanced, we have β i αj i for each ( i, P j ) W. If both i and P j submit C, the platform utility generated from this winning pair is U( i, P j ) = β i αj i. If i submits I and P j submits C, U( i, P j ) = (1 + ζ)β i + (ζ + η)αj i. If i submits C and P j submits I, U( i, P j ) = β i + ηαj i. If both of them submit I, U( i, P j ) = ηαj i. Thus, DFF is transaction-wise budget-balanced. Lemma.3: DFF is computationally efficient. The proof is the same as that of Lemma 4.3 since the two mechanisms have the same time complexity. Lemmas.1.3 complete the proof for Theorem 2. emark.2: Truthfulness is not guaranteed by DFF. For instance, when T i is incomplete and i reports C, P j may benefit by reporting C dishonestly instead of reporting I. Individual rationality is another economic property that DFF does not guarantee. Because when T i is incomplete and P j lies, i s utility is negative even if i reports honestly. 6. PEFOMANCE EVALUATION To evaluate the performance of EFF and DFF, we implemented both mechanisms, and carried out extensive testing on various cases. In both EFF and DFF, we used TASC [33] as the auction mechanism M. We chose Maximum Matching Algorithm [3] as the bipartite matching algorithm used in TASC. The tests were run on a PC with a 3. GHz CPU, and 16 GB memory. emark 6.1: TASC [33] is a double auction based incentive mechanism proposed for cooperative communication, where the relay nodes offer relay services for rewards. TASC is truthful, individually rational, transaction-wise budget-balanced, and computationally efficient. TASC consists of two major steps. First, it applies a bid-independent bipartite matching algorithm to compute an assignment from the buyers to the sellers. Then it applies the McAfee [21] double auction to determine final payments for all buyers and sellers. Performance Metrics: We first studied the impact of the size of users on the running time as a demonstration of 7

8 computational efficiency, and compare the running time of EFF and DFF with that of TASC. We next studied the impact of the size of users and user behavior on the platform utility. Finally, we studied the utilities of providers and requesters. A. Simulation Setup For EFF, we set τ i = 1 for all T i and θ =.1. For DFF, we set ζ =.6 and η =.1. Note that this setting is for simplicity only. For properties of τ i, θ, ζ, and η, see Section 4 and Section, respectively. Valuations, costs, bids, and asks are uniformly distributed over [, 2]. All results in Figs. 1-8 are averaged over, runs for each configuration. To study the behavior of the running time as a function of the number of users, we first fixed m = 1 and let n increase from 1 to 1; then we fixed n = 1 and let m increase from 1 to 1. The corresponding results are reported in Fig. 1 and Fig. 2, respectively. To study the behavior of the platform utility as a function of the number of users, we first fixed m = 1 and let n increase from 1 to 1; then we fixed n = 1 and let m increase from 1 to 1. We chose the scenario where all providers complete their tasks and all individuals are honest (we will present the impact of dishonest users with incomplete tasks on the platform utilities later). In this case, according to Algorithm 1, Algorithm 2, and equation (3.3), the platform utilities in EFF and DFF are the same. Thus, we only show the platform utilities in EFF, and the corresponding results are reported in Fig. 3. To study the platform utility as a function of the percentage of completed tasks in DFF, we fixed m = 1 and n = 1, and let the percentage of completed tasks vary from % to 1% with an increment of %. To study the impact of the number of honest users (who submit honest reports) on platform utility, we first set all requesters to be honest and monitor different percentages of honest providers with different system parameter values of η and ζ in Fig. 4 and Fig., respectively. Then we set all providers to be honest, and monitor different percentages of honest requesters in Fig. 6. To study the platform utility as a function of the percentage of completed tasks in EFF, we fixed m = 1 and n = 1, and let the percentage of completed tasks vary from % to 1% with an increment of %. The corresponding results are presented in Fig. 7 and Fig. 8. To study the utilities of requesters and providers, we set m = n = 1, and ran both EFF and DFF. The corresponding results are reported in Figs. 9-12, where is a randomly picked requester and P is the corresponding provider, with v = 17, c = 4. is matched up with P by the Maximum Matching Algorithm. B. Simulation esults unning Time We study the impact of the size of users on the running time as a demonstration of computational efficiency. Since EFF and DFF have the same running time, we only show the running time of EFF. From Fig. 1, we observe that with the increase of m, the running time increases quadratically when m < n, then grows linearly when m > n. This is because the first part of TASC is a bipartite matching algorithm [3], which takes O(min{m, n} E ) time, where E is the number of edges in the graph and is bounded by O(mn). Let l = min{m, n}. The total time complexity is O(l E +l log l). Thus, in Fig. 1, when m < n = 1, the running time is O(m 2 ); when m > n = 1, the running time is O(m). Fig. 2 can be interpreted by a similar approach. Comparing Fig. 1 and Fig. 2, we observe that the impact from m and n are almost the same. Another observation is that the running time of EFF is almost the same as that of TASC, which implies that applying EFF or DFF does not introduce a high computation cost to M. unning Time (ms) Fig. 1. unning time of EFF and TASC with n = 1 unning Time (ms) m EFF TASC n EFF TASC Fig. 2. unning time of EFF and TASC with m = 1 Platform Utility We study the platform utility of EFF as a function of the number of providers and requesters in Fig. 3. We observe that the platform utility is non-negative, which is guaranteed by the transaction-wise budget-balance of EFF. The platform utility increases at first, then stays steady when m > 1. This is because the platform utility depends on not only the final payments, but also W, which is bounded by min{m, n}. Therefore, when m < n, W increases and the platform utility increases. When W reaches min{m, n}, the platform utility stays at a steady level. Since in this scenario, the platform utilities in EFF and DFF are the same. we only show the platform utilities in EFF. 8

9 Platform Utility n (a) Platform Utility m (b) Fig. 3. Platform utilities in EFF Fig. 3. Platform Utility in EFF: (a) m = 1, (b) n = 1. We study the impact of user behavior on the platform utility in We DFF. study Thethe results impact areof presented user behavior in Fig. on 4, the Fig. platform, and Fig. utility 6, in respectively. DFF. The In results Fig. are 4 and presented Fig., inwe Fig. observe 4, Fig. that, and when Fig. all 6, respectively. requesters areinhonest Fig. in 4 DFF, and Fig. with, the weincreasing observe percentage that when of all requesters completed are tasks, honest the platform in DFF, utility with the decreases. 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Platform Utility Utility % Providers Honest 2% % Providers Honest % 2% Providers Honest 7% % Providers Honest 1% 7% Providers Honest 1% Providers Honest Completion 4 6 % 8 1 Platform Utility Utility % Providers Honest 2% % Providers Honest % 2% Providers Honest Honest 7% % Providers Honest 1% 7% Providers Honest 1% Providers Honest Fig.. Platform utilities in DFF with honest requesters, η =.3 and ζ = 4 Fig.. Platform utilities in DFF with honest requesters, η =.3 and ζ = 4 Platform Utility Utility % equesters Honest 2% % equesters Honest % 2% equesters Honest 7% % equesters Honest 1% 7% equesters Honest 1% equesters Honest Fig. 6. Platform utilities in DFF when all providers are honest Fig. 6. Platform utilities in DFF when all providers are honest utility decreases. This is because with more tasks completed, the tasks, platform the platform collects utility lessdecreases. penaltiesthis fromis the because providers with more for incomplete tasks completed, tasks. 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Platform 6 shows utilities the platform in DFF with utilities honest requesters, of EFF η as= a.1 function and ζ =.6 of the number of completed tasks, and it can be interpreted by a Fig. 6 shows the platform utilities of EFF as a function of 2Completion 4 6 % 8 1 the similar number approach of completed as that of tasks, Fig. and. However, it can beitinterpreted can proved by a theoretically that changing system constants η and ζ would not Fig. 7. Platform utilities in EFF with honest providers similar approach as that of Fig.. However, it can be proved change increasing trend in Fig. 6. Since it is not the major Individual Fig. 7. Platform Utility utilities in EFF with honest providers theoretically that changing system constants η and ζ would not change concernthe in this increasing paper, we trend doinnot Fig. show 6. Since the analysis it nothere. major Individual We monitor Utility the utilities of and P in EFF. 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10 Platform Utility % equesters Honest % % equesters equesters Honest Honest equesters Honest % 1% equesters equesters Honest Honest % Honest 1% equesters Honest 1% equesters Honest Completion % Completion Fig. 8. Platform utilities in EFF with honest requesters Fig. 8. Platform utilities in EFF with honest requesters Fig. 8. Platform utilities in EFF with honest requesters Fig. 9b, we observe that reporting is the dominant strategy Fig. for 9b, we observe According to that Fig. reporting 9a, C would is the report dominant when strategy we for observe that reporting C is the dominant strategy for. reports P. According C. to Thus, both Fig. and 9a, would would report report C C. when Utility P of According reports to Fig. 9a, would report C when P reports C. Utility Thus, both of P and P would report C. C. Thus, both and P would report C. Utility Utility of of Utility Utility of of of P ; ; P P ; ; Utility Utility of P of P 6 6 P P 4 4 ;P ;P 2 2 ;P ;P Comparing the utilities of P task and when it does not complete in DFF when it it completes the the task (represented by task Comparing and whenthe it it does utilities not of complete P in DFF the task when (represented it completes by by the the blue line Fig. 11b and the red line in Fig. 12b, respectively), Bid from Ask from P task blue and linewhen Fig. it 11b does and not the complete red line the in in task Fig. (represented 12b, respectively), by the Bid from Ask from (a) Utility of (b) Utility of P would choose to complete the task for higher utility. (a) (b) blue P would line in choose Fig. 11b toand complete the red the line task in Fig. for a 12b, higher respectively), utility. Fig. 9. Completed tasks in EFF P would choose to complete the task for a higher utility. Fig. 9. Completed tasks in EFF Fig. 7. CONCLUSIONS Fig. 9. Completed 1 shows tasks the in utilities EFF: (a) when Utility Pof Fig. 1 shows the utilities when does, (b) not Utility complete of P. the 7. CONCLUSIONS does not complete the task In this paper, we proposed two novel mechanisms to tackle task Fig. in in1 EFF. EFF. shows Again, Again, the utilities we observe we observe when that that P no no does individual individual not complete can get can get the a 7. CONCLUSIONS a In this paper, we proposed two novel mechanisms to to tackle free-riding and false-reporting in crowdsourcing. We first presented EFF and proved that with arbitration, EFF eliminates task positive positive in EFF. utility utilityagain, increment increment we observe by being by being that dishonest dishonest no individual using usingcan a similar a similar get a In free-riding this paper, andwe false-reporting proposed two in novel crowdsourcing. mechanisms We to first tackle pre- positive analysis analysis utility approach. approach. increment Hence, Hence, by both both being dishonest and P and would would using report report a similar I. I. I. free-riding sented EFFand andfalse-reporting proved that with in crowdsourcing. arbitration, EFF We eliminates first presented free-riding EFF and and false-reporting, proved that with while arbitration, guaranteeing EFF truthfulness, eliminates 1 Utility of free-riding and false-reporting, while guaranteeing truthfulness, analysis approach. Utility of P Hence, both and P would report I. transaction-wise budget-balance, and computational efficiency. 1 free-riding P 1 2 ; transaction-wise and false-reporting, budget-balance, while andguaranteeing computational truthfulness, efficiency. 2 ; We then presented DFF and proved that without arbitra- 2 ; transaction-wise P ; We then presented budget-balance, DFF and proved and computational that without efficiency. arbitra- ; tion, DFF is semi-truthful, while guaranteeing transaction- 4 P 4 ; We tion, then DFFpresented is semi-truthful, DFF and while proved guaranteeing that without transaction- arbitrationwise DFF budget-balance, is semi-truthful, and computational while guaranteeing efficiency. transaction- Another 6 4 wise budget-balance, and computational efficiency. Another 6 ;P ;P feature of our mechanisms is that providers are incentivized 6 ;P is 8 1 ;P wise featurebudget-balance of our mechanisms and computational is that providers efficiency. are incentivized Another 8 1 ;P P to complete their assigned tasks. We implemented both EFF 8 1 ;P feature to complete of our their mechanisms assigned tasks. is that We providers implemented are incentivized both EFF 1 1 P P and DFF. numerical results are presented to study to to 1 Bid from Ask from P andcomplete DFF. Extensive their assigned numerical tasks. results We implemented are presented both to study EFF 1 2 Bid from Ask from the of our proposed (a) Utility Bid from of (a) Utility of (b) Utility Ask from of P (a) (b) Utility of P and the performance DFF. Extensive of our numerical proposedresults mechanisms. are presented to study (b) Fig. 1. Incomplete tasks in EFF the performance of our proposed mechanisms. in EFF EFEENCES Fig. 1. Incomplete tasks in EFF Fig. Comparing 1. Incomplete the tasks utilities in EFF: of P(a) Utility in EFF of when, (b) itutility completes of P it. the EFEENCES the [1] [Online]. Available: Comparing the utilities of P in EFF when it completes task and it does not complete the task (represented by the [1] [2] [Online]. Available: EFEENCES task Comparing and whenthe it does utilities not of complete P in the EFF task when (represented it completes by by the [1] [2] [3] [Online]. Available: Fig. 9b and the red line in Fig. 1b), P would task blueand linewhen Fig. it does 9b and not the complete red line thein task Fig. (represented 1b), P [2] [3] [4] [Online]. Available: choose to complete the task for a higher utility. would by the [3] [4] [] [Online]. P. Azar, J. Available: Chen, and S. Micali, Bayesian auctions, in blue choose linetoincomplete Fig. 9b andtask the for redaline higher in Fig. utility. 1b), P P. J. S. in Next we monitor the utilities of and P in DFF. would From [4] [] [Online]. P. Azar, J. ACM Available: Chen, and ITCS 12, S. Micali, Crowdsourced Bayesian auctions, in pp. pp. choose Next to complete monitor the utilities task for of a higher and utility. P in indff. From Fig. 11, we observe that when P completes the task, there is [] [6] P. Proc. S. S. Azar, ACM J. Chen, ITCS 12, J. J. D. D. and pp. S. Micali, and andb. B. Crowdsourced Sivan, Bayesian auctions, con- con- in [6] Fig. Next 11, we monitor observe the that utilities when P of completes the task, there is is Proc. S. Chawla, ACM in in ITCS J. D. 12, Hartline, pp and B. Sivan, Optimal crowdsourcing con- SODA 12, 12, pp. pp. no incentive for an individual to be dishonest and P when in DFF. the From other [6] [7] S. tests, E. Chawla, in Proc. H. J. D. ACM-SIAM Hartline, and SODA B. 12, ofsivan, pp. public Optimal Public crowdsourcing vol. contests, E. H. Clarke, pp. pp. in Proc. Multipart ACM-SIAM pricing SODA of 12, public pp. goods, Public Choice, vol. 11, 11, no incentive for an individual to be dishonest when the other E. H. of vol. 11, Fig. is honest. 11, wethus, observe both that when and P completes would report thec. task, Utility there of is [7] no is honest. incentive Thus, for both an individual and to P bewould dishonest report when C. Utility of P the other Fig. 12 shows the semi-truthfulness when P fails to complete honest. Thus, both and P would report C. to tocom- pp. K. Deshmukh, [7] [8] [8] E. pp. K. K. H , Clarke, Multipart A. A. V. V. pricing J. J. of D. D. public goods, and andpublic A. A... Choice, vol. 11, [8] is and and 17 33, A. V. Goldberg, J. D. Hartline, and A.. Karlin, Truthful in in pp. pp. Fig. the 12 task shows inthe DFF. semi-truthfulness Again, we observe when Pthat fails there to is complete incentive Fig. the 12for task shows aninindividual the DFF. semi-truthfulness Again, to bewe dishonest observe when when Pthat fails there to other is com- no is [9] and D. DiPalantino and M. Vojnovic, Crowdsourcing and all-pay auctions, in in competitive double no [8] the there is is no [9] [9] K. and D. D. Deshmukh, competitive A. double and and V. Goldberg, auctions, M. M. J. D. in Hartline, ESA 2, and pp. A and and. Karlin, all-pay Truthful pp. pp. auctions, ESA 2, pp plete incentive the for for taskan an inindividual individual DFF. Again, to to be be we dishonest dishonest observewhen that there other other is no is is is in Proc. ACM EC 9, pp honest. Hence, both and P would report I. Utility of [1] [9] D. M. M. DiPalantino C. C. and M. Vojnovic, J. J. Crowdsourcing and andi. I. Stoica, and all-pay auctions, and and [1] M. Feldman, C. Papadimitriou, J. Chuang, and I. Stoica, Free-riding and incentive honest. Hence, for anboth individual to be dishonest would report when I. I. the other is in Proc. ACM in EC 9, in pp IEEE JSAC, vol. vol. 24, 24, honest. Utility of Hence, P and P would report I. [1] M. whitewashing Feldman, C. inpapadimitriou, peer-to-peer systems, J. Chuang, IEEE andjsac, I. Stoica, vol. Free-riding 24, 26. and honest. Hence, both and P would report I. whitewashing in peer-to-peer systems, IEEE JSAC, vol. 24, Utility Utility Utility of of P P of P Utility of Utility of of P ; P ; ; ; P ; ; Bid from (a) Utility Bid Bid from from of Utility of Utility Utility of of P P ;P ;P ;P ;P 1 P 1 ;P ;P 2 P 1 P Ask from P (b) Utility Ask Ask from from of P P P (a) (a) Utility Utility (a) of of (b) (b) Utility Utility (b) of of P Fig. 11. Completed tasks in DFF Fig. 11. Completed tasksin in DFF Fig. 11. Completed tasks in DFF: (a) Utility of, (b) Utility of P. Utility of Utility of of ;P P ; 1 ;P ;P 1 ;P P P ; ; 1 1 P ;P ;P 2 P 1 ; P P Bid 1 from Ask 1from P Bid from Ask from PP Utility of Utility Utility of of P P (a) Utility of (b) Utility of P (a) Utility (a) of (b) Utility (b) of of P Fig. 12. Incomplete tasks in DFF Fig. 12. Incomplete tasksin in DFF Fig. 12. Incomplete tasks in DFF: (a) Utility of, (b) Utility of P. Comparing the utilities of P in DFF when it completes the

11 Xiang Zhang (Student Member 213) received his B.S. degree from University of Science and Technology of China, Hefei, China, in 212. Currently he is a Ph.D student in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. His research interests include network economics and game theory in crowdsourcing and cognitive radio networks. [11] Z. Feng, Y. Zhu, Q. Zhang, H. Zhu, J. Yu, J. Cao, and L. Ni, Towards truthful mechanisms for mobile crowdsourcing with dynamic smartphones, in Proc. IEEE ICDCS 14, pp [12] D. Fundenberg and J.Tirole, Game Theory. MIT, 21. [13] A. V. Goldberg, J. D. Hartline, and A. Wright, Competitive auctions and digital goods, in Proc. ACM-SIAM SODA 1, pp [14] T. Groves, Incentives in Teams, Econometrica, vol. 41, pp. 617, [1] S. Guo, Y. Gu, B. Jiang, and T. He, Opportunistic flooding in lowduty-cycle wireless sensor networks with unreliable links, in Proc. ACM MOBICOM 9. [16] J. 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IEEE INFOCOM 14, pp Guoliang Xue (Member 1996, Senior Member 1999, Fellow, 211) is a Professor of Computer Science and Engineering at Arizona State University. He received the PhD degree (1991) in computer science from the University of Minnesota, Minneapolis, USA. His research interests include survivability, security, and resource allocation issues in networks. He is an Editor of IEEE Network and the Area Editor of IEEE Transactions on Wireless Communications for the area of Wireless Networking. He is an IEEE Fellow. uozhou Yu (Student Member 213) received his B.S. degree from Beijing University of Posts and Telecommunications, Beijing, China, in 213. He is currently pursuing the Ph.D degree in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. His interests include software-defined networking, data center networking, network function virtualization. Dejun Yang (Student Member 28, Member 213) is the Ben L. Fryrear Assistant Professor of Computer Science at Colorado School of Mines. He received his PhD degree from Arizona State University in 213 and BS degree from Peking University in 27. His research interests lie in optimization and economic approaches to networks. He received Best Paper Awards at IEEE ICC 211 and IEEE ICC 212. He has served as a TPC member for many conferences including IEEE INFOCOM. Jian Tang (Member 26, Senior Member 213) is an associate professor in the Department of Electrical Engineering and Computer Science at Syracuse University. He earned his Ph.D degree in Computer Science from Arizona State University in 26. His research interests lie in the areas of Cloud Computing, Big Data and Wireless Networking. He received an NSF CAEE award in 29. He serves as an editor for IEEE Transactions on Vehicular Technology and an editor for IEEE Internet of Things Journal. 11