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.
Looking at the products sold per seller, several studies found evidence of specialisation amongst sellers. Derived from literature review and expert interviews, Kraemer- @cite_30 , for example, promote an ecosystem perspective to understand the actions of underground traders. Comparable to the legitimate business community, underground ecosystems includes actors that compete against each other, targeting competitive advantage. They try to reach this advantage by specialising in a particular type of product @cite_30 .
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Buyers often search across sellers to learn which product best fits their needs. We study how sellers manage these search incentives through their disclosure strategies (e.g., product trials, reviews, and recommendations) and ask how competition affects information provision. If sellers can observe the beliefs of buyers or can coordinate their strategies, then there is an equilibrium in which sellers provide the "monopoly level" of information. In contrast, if buyers' beliefs are private, then there is an equilibrium in which sellers provide full information as search costs vanish. Anonymity and coordination thus play important roles in understanding how advice markets work. Driven by huge monetary reward, some mobile application (app) developers turn to the underground market to buy positive reviews instead of doing legal advertisements. These promotion reviews are either directly posted in app stores like iTunes and Google Play, or published on some popular websites that have many app users. Until now, a clear understanding of this app promotion underground market is still lacking. In this work, we focus on unveiling this underground market and statistically analyzing the promotion incentives, characteristics of promoted apps and suspicious reviewers. To collect promoted apps, we built an automatic data collection system, AppWatcher, which monitored 52 paid review service providers for four months and crawled all the app metadata from their corresponding app stores. Finally, AppWatcher exposes 645 apps promoted in app stores and 29, 680 apps promoted in some popular websites. The current underground market is then reported from various perspectives (e.g., service price, app volume). We identified some interesting features of both promoted apps and suspicious reviewers, which are significantly different from those of randomly chosen apps. Finally, we built a simple tracer to narrow down the suspect list of promoted apps in the underground market. February 2011 saw the emergence of Silk Road, the first successful online anonymous marketplace, in which buyers and sellers could transact with anonymity properties far superior to those available in alternative online or offline means of commerce. Business on Silk Road, primarily involving narcotics trafficking, rapidly boomed, and competitors emerged. At the same time, law enforcement did not sit idle, and eventually managed to shut down Silk Road in October 2013 and arrest its operator. Far from causing the demise of this novel form of commerce, the Silk Road take-down spawned an entire, dynamic, online anonymous marketplace ecosystem, which has continued to evolve to this day. This paper presents a long-term measurement analysis of a large portion of this online anonymous marketplace ecosystem, including 16 different marketplaces, over more than two years (2013- 2015). By using long-term measurements, and combining our own data collection with publicly available previous efforts, we offer a detailed understanding of the growth of the online anonymous marketplace ecosystem. We are able to document the evolution of the types of goods being sold, and assess the effect (or lack thereof) of adversarial events, such as law enforcement operations or large-scale frauds, on the overall size of the economy. We also provide insights into how vendors are diversifying and replicating across marketplaces, and how vendor security practices (e.g., PGP adoption) are evolving. These different aspects help us understand how traditional, physical-world criminal activities are developing an online presence, in the same manner traditional commerce diversified online in the 1990s. 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