Underground online forums are platforms that enable trades of illicit services and stolen goods. Carding forums, in particular, are known for being focused on trading financial information. However, little evidence exists about the sellers that are present on carding forums, the precise types of products they advertise, and the prices buyers pay. Existing literature mainly focuses on the organisation and structure of the forums. Furthermore, studies on carding forums are usually based on literature review, expert interviews, or data from forums that have already been shut down. This paper provides first-of-its-kind empirical evidence on active forums where stolen financial data is traded. We monitored 5 out of 25 discovered forums, collected posts from the forums over a three-month period, and analysed them quantitatively and qualitatively. We focused our analyses on products, prices, seller prolificacy, seller specialisation, and seller reputation.
As discussed, the efforts needed for gaining trust are extensive. A consequence might be that sellers concentrate on establishing reputation on one specific forum instead of several forums. It is therefore not expected that sellers are present on multiple forums. By assuming this, we support Motoyama' @cite_0 expectation of non-existing multiple accounts. In contrast, we disagree with @cite_27 who argue without providing any reasons that sellers would advertise on multiple marketplaces. We hypothesise ( 6 ), therefore, that the vast majority of actors are not operating on more than one forum.
@cite_0 @cite_27
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Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of time. As it is important to do business with trustful individuals and companies, there is a need to survive the reputation concept in virtual markets. Auction sites generally employ reputation systems based on feedbacks that provide a global view to a cyber dealer. In contrast to global trust, people usually infer their personal trust about someone whose reputation is completely or partially unknown by asking their trusted friends. Personal reputation is what makes a person trusted for some people and untrusted for others. There should be a facility for users in a virtual market to specify how much they trust a friend and also a mechanism that infers the trust of a user to another user who is not directly a friend of her. There are two main issues that should be addressed in trust inference. First, the trust modeling and aggregation problem needs to be challenged. Second, algorithms should be introduced to find and select the best paths among the existing trust paths from a source to a sink. First, as trust to a person can be stated more naturally using linguistic expressions, this work suggests employing linguistic terms for trust specification. To this end, corresponding fuzzy sets are defined for trust linguistic terms and a fuzzy trust aggregation method is also proposed. Comparing the fuzzy aggregation method to the existing aggregation methods shows superiority of fuzzy approach especially at aggregating contradictory information. Second, this paper proposes an incremental trust inference algorithm. The results show improvement in preciseness of inference for the proposed inference algorithm over the existing and recently proposed algorithm named TidalTrust. Lack of trust is one of the most frequently cited reasons for consumers not purchasing from Internet vendors. During the last four years a number of empirical studies have investigated the role of trust in the specific context of e-commerce, focusing on different aspects of this multi-dimensional construct. However, empirical research in this area is beset by conflicting conceptualizations of the trust construct, inadequate understanding of the relationships between trust, its antecedents and consequents, and the frequent use of trust scales that are neither theoretically derived nor rigorously validated. The major objective of this paper is to provide an integrative review of the empirical literature on trust in e-commerce in order to allow cumulative analysis of results. The interpretation and comparison of different empirical studies on on-line trust first requires conceptual clarification. A set of trust constructs is proposed that reflects, both institutional phenomena (system trust) and personal and interpersonal forms of trust (dispositional trust, trusting beliefs, trusting intentions and trust-related behaviours), thus facilitating a multi-level and multi-dimensional analysis of research problems related to trust in e-commerce. How should one sell an item to a buyer whose value for the item will only be realized next week? E.g. consider selling a flight to some executive who may or may not have a meeting with a client next week. Suppose that both the seller and the buyer only know a distribution, F, from which the buyer's value, v, for the item will be drawn. One way the seller could go about this sale is to make a take-it-or-leave-it offer today. The offer reads "pay the expected value today to get the item next week". A risk-neutral buyer would find this offer attractive, hence the seller would extract the full surplus. The unsettling feature of the afore-described mechanism is that, for some realizations of v, the bidder ends up with negative utility. In particular, while our mechanism is interim Individually Rational (IR), it is not ex-post IR. How could we fix this? One way is to wait until next week when the value is realized and make a take-it-or-leave-it offer of the item at an optimal monopoly price. The new mechanism is clearly ex-post IR, but its revenue could be much smaller than that of the previous one. Still, this trivial mechanism extracts the best possible revenue among all ex-post IR mechanisms, as a simple argument can establish. However, this optimality argument fails when several items are to be sold over consecutive periods. In this paper, we provide a characterization of the revenue-optimal, ex-post IR, dynamic mechanism selling k items over k periods to a bidder whose values are independent. In particular, we optimize the seller's revenue subject to the following strong individual rationality condition: at each period, the stage utility of the agent, defined to be the surplus from that period's allocation minus the agent's payment, must be non-negative. In particular, the non-negativity of the stage utilities implies that, at the end of each period, the agent's realized utility from participating in the mechanism so far is non-negative. We provide extensions to multiple bidders and an infinite horizon with discount factors. We study the dynamic mechanism design problem of a seller who repeatedly sells independent items to a buyer with private values. In this setting, the seller could potentially extract the entire buyer surplus by running efficient auctions and charging an upfront participation fee at the beginning of the horizon. In some markets, such as internet advertising, participation fees are not practical since buyers expect to inspect items before purchasing them. This motivates us to study the design of dynamic mechanisms under successively more stringent requirements that capture the implicit business constraints of these markets. We first consider a periodic individual rationality constraint, which limits the mechanism to charge at most the buyer's value in each period. While this prevents large upfront participation fees, the seller can still design mechanisms that spread a participation fee across the first few auctions. These mechanisms have the unappealing feature that they provide close-to-zero buyer utility in earlier auctions in exchange for higher utility in later auctions. To address this problem, we introduce a martingale utility constraint, which imposes the requirement that from the perspective of the buyer, the next item's expected utility is equal to the present one's. Our main result is providing a dynamic auction satisfying martingale utility and periodic individual rationality whose loss in profit with respect to first-best (full extraction of buyer surplus) is optimal up to polylogarithmic factors. The proposed mechanism is a dynamic two-tier auction with a hard floor and a soft floor that allocates the item whenever the buyer's bid is above the hard floor and charges the minimum of the bid and the soft floor. Why and to what extent do people make significant purchases from people with whom they have prior noncommercial relationships ? Using data from the economic sociology module of the 1996 General Social Survey, the authors document high levels of within-network exchanges. They argue that transacting with social contacts is effective because it embeds commercial exchanges in a web of obligations and holds the seller's network hostage to appropriate role performance in the economic transaction. It follows that within-network exchanges will be more common in risky transactions that are unlikely to be repeated and in which uncertainty is high. The data support this view. Self-reports about major purchases are consistent with the expectation that exchange frequency reduces the extent of within-network exchanges. Responses to questions about preferences for in-group exchanges support the argument that uncertainty about product and performance quality leads people to prefer sellers with whom they have noncommercial ties. Moreover, people prefer to avoid selling to social contacts under the same conditions that lead buyers to seek such transactions; and people who transact with friends and relatives report greater satisfaction with the results than do people who transact with strangers, especially for risk-laden exchanges This research aims to develop a model of trust and reputation that will ensure good interactions amongst software agents in large scale open systems in particular. The following are key drivers for our model: (1) agents may be self-interested and may provide false accounts of experiences with other agents if it is beneficial for them to do so; (2) agents will need to interact with other agents with which they have no past experience. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents. When there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate. The rapidly increasing popularity of Web 2.0 online communities originates in the ease of collaborative content creation and its sharing. As a result, more community members actively participate in the community and its data growth rates are continuously increasing. This poses the challenge for community platform operators on efficient administration and moderation to ensure the quality of content and to prevent violations of laws (e.g. copyright, privacy, illegal content) and community rules. Involvement of employed administrators who read and approve every piece of user-generated content is clearly the safest way of quality assurance. Since this is a time consuming task it does not scale up with Web 2.0 dimensions. So administrative functions are delegated to members of the community, the moderators. The strategy for choosing trustworthy moderators in big anonymous communities is specified in policies based on user reputation that is measured in bonus points. The proper balancing between community self-management and administration is crucial for the quality, attractiveness, and scalability of the entire community. Therefore understanding the mutual influences between community actors, reputation systems and platform policies that employ user reputation is crucial to ensure the overall quality, user acceptance, and success of the entire online community. Our objective is to predict the behavior in an online community for different policies, which may result in different overall quality of the community content. For this purpose we present our community analysis framework CoSiMo (an acronym for Community Simulation and Modeling), which employs the model-based approach for predicting the impact of policies on community dynamics and health. Through systematic variation of simulated quality assurance mechanisms we show that our model plausibly captures the influence of policies to content quality and can be therefore exploited for optimization of real online communities. Firms using online advertising regularly run experiments with multiple versions of their ads since they are uncertain about which ones are most effective. Within a campaign, firms try to adapt to intermediate results of their tests, optimizing what they earn while learning about their ads. But how should they decide what percentage of impressions to allocate to each ad? This paper answers that question, resolving the well-known "learn-and-earn'' trade-off using multi-armed bandit (MAB) methods. The online advertiser's MAB problem, however, contains particular challenges, such as a hierarchical structure (ads within a website), attributes of actions (creative elements of an ad), and batched decisions (millions of impressions at a time), that are not fully accommodated by existing MAB methods. Our approach captures how the impact of observable ad attributes on ad effectiveness differs by website in unobserved ways, and our policy generates allocations of impressions that can be used in practice. We implemented this policy in a live field experiment delivering over 700 million ad impressions in an online display campaign with a large retail bank. Over the course of two months, our policy achieved an 8 improvement in the customer acquisition rate, relative to a control policy, without any additional costs to the bank. Beyond the actual experiment, we performed counterfactual simulations to evaluate a range of alternative model specifications and allocation rules in MAB policies. Finally, we show that customer acquisition would decrease about 10 if the firm were to optimize click through rates instead of conversion directly, a finding that has implications for understanding the marketing funnel. We consider the problem of designing revenue-maximizing online posted-price mechanisms when the seller has limited supply. A seller has k identical items for sale and is facing n potential buyers (“agents”) that are arriving sequentially. Each agent is interested in buying one item. Each agent’s value for an item is an independent sample from some fixed (but unknown) distribution with support [0,1]. The seller offers a take-it-or-leave-it price to each arriving agent (possibly different for different agents), and aims to maximize his expected revenue. We focus on mechanisms that do not use any information about the distribution; such mechanisms are called detail-free (or prior-independent). They are desirable because knowing the distribution is unrealistic in many practical scenarios. We study how the revenue of such mechanisms compares to the revenue of the optimal offline mechanism that knows the distribution (“offline benchmark”). We present a detail-free online posted-price mechanism whose revenue is at most O((k log n)2 3) less than the offline benchmark, for every distribution that is regular. In fact, this guarantee holds without any assumptions if the benchmark is relaxed to fixed-price mechanisms. Further, we prove a matching lower bound. The performance guarantee for the same mechanism can be improved to O(√k log n), with a distribution-dependent constant, if the ratio k n is sufficiently small. We show that, in the worst case over all demand distributions, this is essentially the best rate that can be obtained with a distribution-specific constant. On a technical level, we exploit the connection to multiarmed bandits (MAB). While dynamic pricing with unlimited supply can easily be seen as an MAB problem, the intuition behind MAB approaches breaks when applied to the setting with limited supply. Our high-level conceptual contribution is that even the limited supply setting can be fruitfully treated as a bandit problem. We study double auction market design where the market maker wants to maximize its total revenue by buying low from the sell- ers and selling high to the buyers. We consider a Bayesian setting where buyers and sellers have independent probability distributions on the val- ues of products on the market. For the simplest setting where each seller has one kind of item that can be sold in whole to a buyer, and each buyer's value can be represented by a single parameter, i.e., single-parameter setting, we develop a maximum mechanism for the market maker to maximize its own revenue. For the more general case where the product may be different, we consider various models in terms of supplies and demands constraints. For each of them, we develop a polynomial time computable truthful mechanism for the market maker to achieve a revenue at least a constant α times the revenue of any other truthful mechanism. We consider a double auction market maker who collects valuations from buy- ers and sellers about a certain product to decide on the prices each seller gets and each buyer pays. The buyers may want to buy many units and the sellers may have many units to part with. The buyers and sellers may have different valuations of the product, and there is public knowledge of the probability dis- tributions of the valuations (but each valuation, sampled from its distribution, is known only to its own buyer or seller). For simplicity, we assume that the probability distributions are independent. For the sellers and buyers, they know their own private values exactly. The market maker purchases the products from the sellers and sell them to the buyers. Our goal is to design a market mechanism that maximizes the revenue of the market maker. In other words, the market maker is to buy the same amount of products from the sellers as the amount sold to the buyers with the objective of maximizing the difference of its collected payment from the buyers and the total amount paid to the sellers. When in addi- tion we assume public knowledge of distributions of buyers' private values from the previous sales, we call it a revenue maximization Bayesian double auction market maker. � Supported by EPSRC grant EP G069239 1 "Efficient Decentralised Approaches in We consider the problem of designing budget feasible mechanisms for a dealer, who aims to maximize revenue by buying items from a seller market and selling them to a buyer market that consists of unit-demand buyers. Different from the related literature, the dealer's value'' for a set of items that he purchased from the seller market is not directly given as a number but it is defined to be the maximum revenue the dealer can obtain from selling the items to the buyers. We aim to design mechanisms that are dominant-strategy truthful for the sellers to report their costs and envy-free for the buyers to purchase their most preferred items (given their prices) in the final outcome, such that the total payment to the sellers does not exceed the dealer's budget and the dealer's revenue is (approximately) maximized. First, to understand the structure of the optimal mechanisms, we show that the maximum (envy-free) revenue obtainable by the dealer as a function of the set of purchased items is monotone and subadditive. Thus, existing results on subadditive optimization problems are potentially applicable in solving the mechanism design problem for the dealer. However, a crucial assumption adopted by all previous studies on subadditive functions is that the mechanism or algorithm has access to the value oracle and or the demand oracle. In the dealer's problem, instead, we show that (1) the demand oracle can be efficiently simulated by the value oracle and (2) both have efficient O(log n)-approximation algorithms, where n is the number of buyers. This is particularly interesting given the literature, since, in general, the demand oracle can always efficiently simulate the value oracle, and there are cases where the demand oracle is strictly more powerful. Our results show that, for the dealer's problem, the two oracles are as powerful as each other. Finally, using the results above, we provide a polynomial-time budget feasible mechanism for the dealer that doesn't use any oracle and provides an O((log2n)(log2m))-approximation of the optimal revenue, where m is the number of sellers. Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy. The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently as a decentralized multi-armed bandit (D-MAB) problem. In a D-MAB problem there are @math users and @math arms (channels) that each offer i.i.d. stochastic rewards with unknown means so long as they are accessed without collision. The goal is to design a decentralized online learning policy that incurs minimal regret, defined as the difference between the total expected rewards accumulated by a model-aware genie, and that obtained by all users applying the policy. We make two contributions in this paper. First, we consider the setting where the users have a prioritized ranking, such that it is desired for the @math -th-ranked user to learn to access the arm offering the @math -th highest mean reward. For this problem, we present the first distributed policy that yields regret that is uniformly logarithmic over time without requiring any prior assumption about the mean rewards. Second, we consider the case when a fair access policy is required, i.e., it is desired for all users to experience the same mean reward. For this problem, we present a distributed policy that yields order-optimal regret scaling with respect to the number of users and arms, better than previously proposed policies in the literature. Both of our distributed policies make use of an innovative modification of the well known UCB1 policy for the classic multi-armed bandit problem that allows a single user to learn how to play the arm that yields the @math -th largest mean reward. Managing trust is a problem of particular importance in peer-to-peer environments where one frequently encounters unknown agents. Existing methods for trust management, that are based on reputation, focus on the semantic properties of the trust model. They do not scale as they either rely on a central database or require to maintain global knowledge at each agent to provide data on earlier interactions. In this paper we present an approach that addresses the problem of reputation-based trust management at both the data management and the semantic level. We employ at both levels scalable data structures and algorithms that require no central control and allow to assess trust by computing an agents reputation from its former interactions with other agents. Thus the meethod can be implemented in a peer-to-peer environment and scales well for very large numbers of participants. We expect that scalable methods for trust management are an important factor, if fully decentralized peer-to-peer systems should become the platform for more serious applications than simple file exchange. One of the earliest and best known Internet reputation systems is run by eBay, which gathers comments from buyers and sellers about each other after each transaction. Examination of a large data set from 1999 reveals several interesting features. First, despite incentives to free ride, feedback was provided more than half the time. Second, well beyond reasonable expectation, it was almost always positive. Third, reputation profiles were predictive of future performance, though eBay's net feedback statistic is far from the best predictor available. Fourth, there was a high correlation between buyer and seller feedback, suggesting that the players reciprocate and ret aliate. Since there is, in principle, no reason why third parties should not pay individuals for the use of their data, we introduce a realistic market that would allow these payments to be made while taking into account the privacy attitude of the participants. And since it is usually important to use unbiased samples to obtain credible statistical results, we examine the properties that such a market should have and suggest a mechanism that compensates those individuals that participate according to their risk attitudes. Equally important, we show that this mechanism also benefits buyers, as they pay less for the data than they would if they compensated all individuals with the same maximum fee that the most concerned ones expect. Analyzing network associations with performance in three study populations, I find that secondhand brokerage--moving information between people to whom one is only connected indirectly--often has little or no value. Brokerage benefits are dramatically concentrated in the immediate network around a person. Why that is so, and conditions under which it is more or less so, are examined. The research implication is that brokerage can be measured with designs in which data are limited to an immediate network. The theory implication is that the social capital of brokerage is a local phenomenon mirrored in the Austrian market metaphor's emphasis on tacit, local knowledge. [ABSTRACT FROM AUTHOR] Copyright of Academy of Management Journal is the property of Academy of Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems. For Bayesian combinatorial auctions, we present a general framework for approximately reducing the mechanism design problem for multiple buyers to single buyer sub-problems. Our framework can be applied to any setting which roughly satisfies the following assumptions: (i) buyers' types must be distributed independently (not necessarily identically), (ii) objective function must be linearly separable over the buyers, and (iii) except for the supply constraints, there should be no other inter-buyer constraints. Our framework is general in the sense that it makes no explicit assumption about buyers' valuations, type distributions, and single buyer constraints (e.g., budget, incentive compatibility, etc). We present two generic multi buyer mechanisms which use single buyer mechanisms as black boxes; if an @math -approximate single buyer mechanism can be constructed for each buyer, and if no buyer requires more than @math of all units of each item, then our generic multi buyer mechanisms are @math -approximation of the optimal multi buyer mechanism, where @math is a constant which is at least @math . Observe that @math is at least 1 2 (for @math ) and approaches 1 as @math . As a byproduct of our construction, we present a generalization of prophet inequalities. Furthermore, as applications of our framework, we present multi buyer mechanisms with improved approximation factor for several settings from the literature. Different information sources publish information with different degrees of correctness and originality. False information can often result in considerable damage. Hence, trustworthinessof information is an important issue in this datadriven world economy. Reputation of different agents in a network has been studied earlier in a variety of domains like e-commerce, social sciences, sensor networks, and P2P networks. Recently there has been work in the data mining community on performing trust analysis based on the data provided by multiple information providers for different objects, and such agents and their provided information about data objects form a multi-typed heterogeneous network. The trust analysis under such a framework is considered as heterogeneous network-based trust analysis. This paper will survey heterogeneous network-based trust analysis models and their applications. We would conclude with a summary and some thoughts on future research in the area.