Knowledge Discovery in Cryptocurrency Transactions: A Survey

Abstract

Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.

Detecting Roles of Money Laundering in Bitcoin Mixing Transactions: A Goal Modeling and Mining Framework

“…For example, Chen et al [6] reviewed the status, trends, and challenges in blockchain data analysis and summarized seven typical research issues of cryptocurrency transaction analysis into entity recognition, privacy identification, network risk parsing, network visualization and portrait, analysis of cryptocurrency market, etc. Liu et al [7] surveyed knowledge discovery in cryptocurrency transactions and summarized the existing research that uses data mining techniques into three aspects, including transaction tracing and blockchain address linking, the analysis of collective user behaviors, and the study of individual user behaviors.…”

The Rich Still Get Richer: Empirical Comparison of Preferential Attachment via Linking Statistics in Bitcoin and Ethereum

“…As it is common practice to create new addresses regularly (it is often advised not to reuse addresses), there have been a significant research interest in trying to identify groups of addresses that belong to the same user, or are controlled by the same entity (Wu et al, 2020;Fischer et al, 2021;Liu et al, 2021). At the same time, there are several known methodologies and services that aim to "mix" bitcoins in a way that limits the possibility of such grouping and tracking the flow of money as well (Bonneau et al, 2015;Heilman et al, 2017;Phetsouvanh et al, 2019).…”

“…Cryptocurrencies have presented a disruptive change for both economics and computer science. Over the past years, interest in cryptocurrencies resulted in a huge amount of money invested in them (Baur et al, 2018;Begušić et al, 2018) and a growing amount of research carried out on diverse application possibilities of the underlying technologies, e.g., blockchain and decentralized trust (Bonneau et al, 2015;Yli-Huumo et al, 2016;Zheng et al, 2016;Seres et al, 2020;Liu et al, 2021). At the same time, cryptocurrencies provide a unique opportunity as financial systems where the whole list of transactions is exposed, making possible to study the dynamic interactions taking place in them (Kondor et al, 2014a;Phetsouvanh et al, 2019;Oggier et al, 2020;Wu et al, 2020); this allows the study of the complete history of how novel, alternative financial systems evolve from their inception (Seebacher and Maleshkova, 2018;Dixon et al, 2019).…”

Transdisciplinary Scientific Strategies for Soft Computing Development: Towards an Era of Data and Business Analytics

“…That means that the respective dual characteristics of those disciplines and processes: database-data analyst, expert-knowledge engineer, and decision maker-decision analyst are joined in just one dual process: sources-analyst by wide knowledge discovery (Figure 1) [82][83][84][85].…”