The design of data markets has gained importance as firms increasingly use machine learning models fueled by externally acquired training data. A key consideration is the externalities firms face when data, though inherently freely replicable, is allocated to competing firms. In this setting, we demonstrate that a data seller's optimal revenue increases as firms can pay to prevent allocations to others. To do so, we first reduce the combinatorial problem of allocating and pricing multiple datasets to the auction of a single digital good by modeling utility for data through the increase in prediction accuracy it provides. We then derive welfare and revenue maximizing mechanisms, highlighting how the form of firms' private information - whether the externalities one exerts on others is known, or vice-versa - affects the resulting structures. In all cases, under appropriate assumptions, the optimal allocation rule is a single threshold per firm, where either all data is allocated or none is.
In many settings agents participate in multiple different auctions that are not necessarily implemented simultaneously. Future opportunities affect strategic considerations of the players in each auction, introducing externalities. Motivated by this consideration, we study a setting of a market of buyers and sellers, where each seller holds one item, bidders have combinatorial valuations and sellers hold item auctions sequentially. Our results are qualitatively different from those of simultaneous auctions, proving that simultaneity is a crucial aspect of previous work. We prove that if seller…
In digital goods auctions, there is an auctioneer who sells an item with unlimited supply to a set of potential buyers, and the objective is to design truthful auction to maximize the total profit of the auctioneer. Motivated from an observation that the values of buyers for the item could be interconnected through social networks, we study digital goods auctions with positive externalities among the buyers. This defines a multi-parameter auction design problem where the private valuation of every buyer is a function of other winning buyers. The main contribution of this paper is a truthful co…
Sponsored Search Auctions (SSAs) arguably represent the problem at the intersection of computer science and economics with the deepest applications in real life. Within the realm of SSAs, the study of the effects that showing one ad has on the other ads, a.k.a. externalities in economics, is of utmost importance and has so far attracted the attention of much research. However, even the basic question of modeling the problem has so far escaped a definitive answer. The popular cascade model is arguably too idealized to really describe the phenomenon yet it allows a good comprehension of the prob…
This paper presents models for predicted click-through rates in position auctions that take into account two possibilities that are not normally considered---that the identities of ads shown in other positions may affect the probability that an ad in a particular position receives a click (externalities) and that some ads may be less adversely affected by being shown in a lower position than others (brand effects). We present a general axiomatic methodology for how click probabilities are affected by the qualities of the ads in the other positions, and illustrate that using these axioms will i…
Sponsored search auctions constitute one of the most successful applications of microeconomic mechanisms. In mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and to assure both the advertisers and the auctioneer a non-negative utility. Nonetheless, in sponsored search auctions, the click-through-rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard truthful mechanisms cannot be directly applied and must be paired with an effective learning algorithm for the estimation of the CTRs. This introduces the cri…
Motivated by the problem of market power in electricity markets, we introduced in previous works a mechanism for simplified markets of two agents with linear cost. In standard procurement auctions, the market power resulting from the quadratic transmission losses allows the producers to bid above their true values, which are their production cost. The mechanism proposed in the previous paper optimally reduces the producers' margin to the society's benefit. In this paper, we extend those results to a more general market made of a finite number of agents with piecewise linear cost functions, whi…
Combinatorial auctions (CA) are a well-studied area in algorithmic mechanism design. However, contrary to the standard model, empirical studies suggest that a bidder's valuation often does not depend solely on the goods assigned to him. For instance, in adwords auctions an advertiser might not want his ads to be displayed next to his competitors' ads. In this paper, we propose and analyze several natural graph-theoretic models that incorporate such negative externalities, in which bidders form a directed conflict graph with maximum out-degree $Δ$. We design algorithms and truthful mechanisms f…
Blockchain, an emerging decentralized security system, has been applied in many applications, such as bitcoin, smart grid, and Internet-of-Things. However, running the mining process may cost too much energy consumption and computing resource usage on handheld devices, which restricts the use of blockchain in mobile environments. In this paper, we consider deploying edge computing service to support the mobile blockchain. We propose an auction-based edge computing resource market of the edge computing service provider. Since there is competition among miners, the allocative externalities (posi…
As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of "block mining" by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog c…
Markets for zero-day exploits (software vulnerabilities unknown to the vendor) have a long history and a growing popularity. We study these markets from a revenue-maximizing mechanism design perspective. We first propose a theoretical model for zero-day exploits markets. In our model, one exploit is being sold to multiple buyers. There are two kinds of buyers, which we call the defenders and the offenders. The defenders are buyers who buy vulnerabilities in order to fix them (e.g., software vendors). The offenders, on the other hand, are buyers who intend to utilize the exploits (e.g., nationa…
In this paper, we analyse how a peer-to-peer sharing platform should price its service (when imagined as an excludable public good) to maximize profit, when each user's participation adds value to the platform service by creating a positive externality to other participants. To characterize network externalities as a function of the number of participants, we consider different bounded and unbounded user utility models. The bounded utility model fits many infrastructure sharing applications with bounded network value, in which complete coverage has a finite user valuation (e.g., WiFi or hotspo…
A big data service is any data-originated resource that is offered over the Internet. The performance of a big data service depends on the data bought from the data collectors. However, the problem of optimal pricing and data allocation in big data services is not well-studied. In this paper, we propose an auction-based big data market model. We first define the data cost and utility based on the impact of data size on the performance of big data analytics, e.g., machine learning algorithms. The big data services are considered as digital goods and uniquely characterized with "unlimited supply…
In mechanism design, it is challenging to design the optimal auction with correlated values in general settings. Although value distribution can be further exploited to improve revenue, the complex correlation structure makes it hard to acquire in practice. Data-driven auction mechanisms, powered by machine learning, enable to design auctions directly from historical auction data, without relying on specific value distributions. In this work, we design a learning-based auction, which can encode the correlation of values into the rank score of each bidder, and further adjust the ranking rule to…
This paper develops a general approach, rooted in statistical learning theory, to learning an approximately revenue-maximizing auction from data. We introduce $t$-level auctions to interpolate between simple auctions, such as welfare maximization with reserve prices, and optimal auctions, thereby balancing the competing demands of expressivity and simplicity. We prove that such auctions have small representation error, in the sense that for every product distribution $F$ over bidders' valuations, there exists a $t$-level auction with small $t$ and expected revenue close to optimal. We show tha…
We study a general class of repeated auctions, such as the ones found in electricity markets, as multi-agent games between the bidders. In such a repeated setting, bidders can adapt their strategies online based on the data observed in the previous auction rounds. Moreover, if no-regret algorithms are employed by the bidders to update their strategies, the game is known to converge to a coarse-correlated equilibrium, which generalizes the notion of Nash equilibrium to a probabilistic view of the auction state. Well-studied no-regret algorithms depend on the feedback information available at ev…
Consider Myerson's optimal auction with respect to an inaccurate prior, e.g., estimated from data, which is an underestimation of the true value distribution. Can the auctioneer expect getting at least the optimal revenue w.r.t. the inaccurate prior since the true value distribution is larger? This so-called strong revenue monotonicity is known to be true for single-parameter auctions when the feasible allocations form a matroid. We find that strong revenue monotonicity fails to generalize beyond the matroid setting, and further show that auctions in the matroid setting are the only downward-c…
Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a flexible and scalable sensing coverage with a low deployment cost, by employing mobile users/devices to perform sensing tasks. In this work, we propose a novel MCS framework with data reuse, where multiple tasks with common data requirement can share (reuse) the common data with each other through an MCS platform. We study the optimal assignment of mobile users and tasks (with data reuse) systematically, under both information symmetry and asymmetry, depending on whether the user cost and the task valuation are public…
Private data query combines mechanism design with privacy protection to produce aggregated statistics from privately-owned data records. The problem arises in a data marketplace where data owners have personalised privacy requirements and private data valuations. We focus on the case when the data owners are single-minded, i.e., they are willing to release their data only if the data broker guarantees to meet their announced privacy requirements. For a data broker who wants to purchase data from such data owners, we propose the SingleMindedQuery (SMQ) mechanism, which uses a reverse auction to…
In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this paper, we propose an auction based market model for incentivizing data…
Good economic mechanisms depend on the preferences of participants in the mechanism. For example, the revenue-optimal auction for selling an item is parameterized by a reserve price, and the appropriate reserve price depends on how much the bidders are willing to pay. A mechanism designer can potentially learn about the participants' preferences by observing historical data from the mechanism; the designer could then update the mechanism in response to learned preferences to improve its performance. The challenge of such an approach is that the data corresponds to the actions of the participan…
In decentralized systems, nodes often need to coordinate to access shared resources in a fair manner. One approach to perform such arbitration is to rely on auction mechanisms. Although there is an extensive literature that studies auctions, most of these works assume the existence of a central, trusted auctioneer. Unfortunately, in fully decentralized systems, where the nodes that need to cooperate operate under separate spheres of control, such central trusted entity may not exist. Notable examples of such decentralized systems include community networks, clouds of clouds, cooperative nano d…
This paper develops the theory of mechanism redesign by which an auctioneer can reoptimize an auction based on bid data collected from previous iterations of the auction on bidders from the same market. We give a direct method for estimation of the revenue of a counterfactual auction from the bids in the current auction. The estimator is a simple weighted order statistic of the bids and has the optimal error rate. Two applications of our estimator are A/B testing (a.k.a., randomized controlled trials) and instrumented optimization (i.e., revenue optimization subject to being able to do accurat…
In this study, we apply reinforcement learning techniques and propose what we call reinforcement mechanism design to tackle the dynamic pricing problem in sponsored search auctions. In contrast to previous game-theoretical approaches that heavily rely on rationality and common knowledge among the bidders, we take a data-driven approach, and try to learn, over repeated interactions, the set of optimal reserve prices. We implement our approach within the current sponsored search framework of a major search engine: we first train a buyer behavior model, via a real bidding data set, that accuratel…
Joint radar and communication (JRC) is a promising technique for spectrum re-utilization, which enables radar sensing and data transmission to operate on the same frequencies and the same devices. However, due to the multi-objective property of JRC systems, channel allocation to JRC nodes should be carefully designed to maximize system performance. Additionally, because of the broadcast nature of wireless signals, a watchful adversary, i.e., a warden, can detect ongoing transmissions and attack the system. Thus, we develop a covert JRC system that minimizes the detection probability by wardens…
Motivated by online display ad exchanges, we study a setting in which an exchange repeatedly interacts with bidders who have quota, making decisions about which subsets of bidders are called to participate in ad-slot-specific auctions. A bidder with quota cannot respond to more than a certain number of calls per second. In practice, random throttling is the principal solution by which these constraints are enforced. Given the repeated nature of the interaction with its bidders, the exchange has access to data containing information about each bidder's segments of interest. This information can…
This paper studies equilibrium quality of semi-separable position auctions (known as the Ad Types setting) with greedy or optimal allocation combined with generalized second-price (GSP) or Vickrey-Clarke-Groves (VCG) pricing. We make three contributions: first, we give upper and lower bounds on the Price of Anarchy (PoA) for auctions which use greedy allocation with GSP pricing, greedy allocations with VCG pricing, and optimal allocation with GSP pricing. Second, we give Bayes-Nash equilibrium characterizations for two-player, two-slot instances (for all auction formats) and show that there ex…
We introduce a new numerical framework to learn optimal bidding strategies in repeated auctions when the seller uses past bids to optimize her mechanism. Crucially, we do not assume that the bidders know what optimization mechanism is used by the seller. We recover essentially all state-of-the-art analytical results for the single-item framework derived previously in the setup where the bidder knows the optimization mechanism used by the seller and extend our approach to multi-item settings, in which no optimal shading strategies were previously known. Our approach yields substantial increases…
We consider a scenario in which a database stores sensitive data of users and an analyst wants to estimate statistics of the data. The users may suffer a cost when their data are used in which case they should be compensated. The analyst wishes to get an accurate estimate, while the users want to maximize their utility. We want to design a mechanism that can estimate statistics accurately without compromising users' privacy. Since users' costs and sensitive data may be correlated, it is important to protect the privacy of both data and cost. We model this correlation by assuming that a user's …
In this work, we investigate online mechanisms for trading time-sensitive valued data. We adopt a continuous function $d(t)$ to represent the data value fluctuation over time $t$. Our objective is to design an \emph{online} mechanism achieving \emph{truthfulness} and \emph{revenue-competitiveness}. We first prove several lower bounds on the revenue competitive ratios under various assumptions. We then propose several online truthful auction mechanisms for various adversarial models, such as a randomized observe-then-select mechanism $\mathcal{M}_1$ and prove that it is \textit{truthful} and $Θ…
Device-to-Device communication allows a cellular user (relay node) to relay data between the base station (BS) and another cellular user (destination node). We address the problem of designing reverse auctions to assign a relay node to each destination node, when there are multiple potential relay nodes and multiple destination nodes, in the scenarios where the transmission powers of the relay nodes are: 1) fixed, 2) selected to achieve the data rates desired by destination nodes, and 3) selected so as to approximately maximize the BS's utility. We show that auctions based on the widely used V…
Device-to-Device (D2D) communication is offering smart phone users a choice to share files with each other without communicating with the cellular network. In this paper, we discuss the behaviors of two characters in the D2D data transaction model from an economic point of view: the data buyers who wish to buy a certain quantity of data, as well as the data sellers who wish to sell data through the D2D network. The optimal price and purchasing strategies are analyzed and deduced based on game theory.
We study a market for private data in which a data analyst publicly releases a statistic over a database of private information. Individuals that own the data incur a cost for their loss of privacy proportional to the differential privacy guarantee given by the analyst at the time of the release. The analyst incentivizes individuals by compensating them, giving rise to a \emph{privacy auction}. Motivated by recommender systems, the statistic we consider is a linear predictor function with publicly known weights. The statistic can be viewed as a prediction of the unknown data of a new individua…
Standard ad auction formats do not immediately extend to settings where multiple size configurations and layouts are available to advertisers. In these settings, the sale of web advertising space increasingly resembles a combinatorial auction with complementarities, where truthful auctions such as the Vickrey-Clarke-Groves (VCG) can yield unacceptably low revenue. We therefore study core selecting auctions, which boost revenue by setting payments so that no group of agents, including the auctioneer, can jointly improve their utilities by switching to a different outcome. Our main result is a c…
Statistically Truthful Auctions via Acceptance Rule
Department of Electrical and Computer Engineering, Technion IIT, Israel（电气与计算机工程系，技术学院IIT，以色列）；Department of Computer Science, Technion IIT, Israel（计算机科学系，技术学院IIT，以色列）
Secondary spectrum auction is widely applied in wireless networks for mitigating the spectrum scarcity. In a realistic spectrum trading market, the requests from secondary users often specify the usage of a fixed spectrum frequency band in a certain geographical region and require a duration time in a fixed available time interval. Considering the selfish behaviors of secondary users, it is imperative to design a truthful auction which matches the available spectrums and requests of secondary users optimally. Unfortunately, existing designs either do not consider spectrum heterogeneity or igno…
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 …
As cellular operators are suffering from a data explosion problem, and users are consequently experiencing poor data services, the introduction of femtocells offers a cost-effective way to mitigate this problem. Femtocells enable larger network capacity by increasing spatial reuse of the spectrum and shortening the distance to the users. Existing work has shown that open access femtocells, which allow unregistered macro users to connect, are efficient in reducing inter-cell interference and offloading traffic. However, a major obstacle constraining the potential capability of femtocells and op…
Intelligent Agents for Auction-based Federated Learning: A Survey
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；Department of Computer Science, Bar-Ilan University（巴伊兰大学计算机科学系）
Korea University（高丽大学）；Hallym University（翰林大学）；California State University, Long Beach（加州州立大学长滩分校）
A successful deployment of drones provides an ideal solution for surveillance systems. Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage. Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions. This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight const…
We consider the non-metric data placement problem and develop distributed algorithms for computing or approximating its optimal integral solution. We first show that the non-metric data placement problem is inapproximable up to a logarithmic factor. We then provide a game-theoretic decomposition of the objective function and show that natural Glauber dynamics in which players update their resources with probability proportional to the utility they receive from caching those resources will converge to an optimal global solution for a sufficiently large noise parameter. In particular, we establi…
