There’s No Free Lunch, Even Using Bitcoin: Tracking the Popularity and Profits of Virtual Currency Scams

Abstract

Abstract. We present the first empirical analysis of Bitcoin-based scams: operations established with fraudulent intent. By amalgamating reports gathered by voluntary vigilantes and tracked in online forums, we identify 192 scams and categorize them into four groups: Ponzi schemes, mining scams, scam wallets and fraudulent exchanges. In 21% of the cases, we also found the associated Bitcoin addresses, which enables us to track payments into and out of the scams. We find that at least $11 million has been contributed to the scams from 13 000 distinct victims. Furthermore, we present evidence that the most successful scams depend on large contributions from a very small number of victims. Finally, we discuss ways in which the scams could be countered.

Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network

“…Thus, mining the blockchain data to detect scams is also a critical topic. Recently, there are many studies on this topic, such as Bitcoin-based scams [20], the smart contract based Ponzi schemes [21], [22], money laundry [23], attacks [24]. See [25] for a full survey of this topic.…”

BlockTag: Design and Applications of a Tagging System for Blockchain Analysis

“…Organizations responsible for consumer protection, such as trade commission agencies and financial regulatory authorities, have a mandate to research and identify fraud cases involving cryptocurrencies, including unlawful initial coin offerings and Ponzi schemes. Given the popularity of Ponzi schemes in Bitcoin [28,29], we focus on this type of fraud and show how BlockTag can help analysts flag users who are likely victims or operators of such schemes.…”

Short Paper: An Exploration of Code Diversity in the Cryptocurrency Landscape

“…In terms of the latter, there are by now many papers that have focused on measuring properties of both the peer-to-peer networks [17,7,3,1] and the blockchain data associated with cryptocurrencies [26,27,19,28,21,13,5,31], as well as their broader ecosystem of participants [20,34,32,33]. Given the volume of research, we focus only on those papers most related to our own, in that they analyze properties across multiple cryptocurrencies, rather than within a single one like Bitcoin.…”