Annotating blockchains with auxiliary data is useful for many applications. For example, e-crime investigations of illegal Tor hidden services, such as Silk Road, often involve linking Bitcoin addresses, from which money is sent or received, to user accounts and related online activities. We present BlockTag, an open-source tagging system for blockchains that facilitates such tasks. We describe BlockTag's design and present three analyses that illustrate its capabilities in the context of privacy research and law enforcement.
@cite_28: Graph databases (GDB) are now a viable alternative to Relational Database Systems (RDBMS). Chemistry, biology, semantic web, social networking and recommendation engines are all examples of applications that can be represented in a much more natural form. Comparisons will be drawn between relational database systems (Oracle, MySQL) and graph databases (Neo4J) focusing on aspects such as data structures, data model features and query facilities. Additionally, several of the inherent and contemporary limitations of current offerings comparing and contrasting graph vs. relational database implementations will be explored.
@cite_18: Analysis of blockchain data is useful for both scientific research and commercial applications. We present BlockSci, an open-source software platform for blockchain analysis. BlockSci is versatile in its support for different blockchains and analysis tasks. It incorporates an in-memory, analytical (rather than transactional) database, making it several hundred times faster than existing tools. We describe BlockSci's design and present four analyses that illustrate its capabilities. This is a working paper that accompanies the first public release of BlockSci, available at this https URL We seek input from the community to further develop the software and explore other potential applications.
@cite_17: Bitcoin, the famous peer-to-peer, decentralized electronic currency system, allows users to benefit from pseudonymity, by generating an arbitrary number of aliases (or addresses) to move funds. However, the complete history of all transactions ever performed, called “blockchain”, is public and replicated on each node. The data it contains is difficult to analyze manually, but can yield a high number of relevant information. In this paper we present a modular framework, BitIodine, which parses the blockchain, clusters addresses that are likely to belong to a same user or group of users, classifies such users and labels them, and finally visualizes complex information extracted from the Bitcoin network. BitIodine labels users semi-automatically with information on their identity and actions which is automatically scraped from openly available information sources. BitIodine also supports manual investigation by finding paths and reverse paths between addresses or users. We tested BitIodine on several real-world use cases, identified an address likely to belong to the encrypted Silk Road cold wallet, or investigated the CryptoLocker ransomware and accurately quantified the number of ransoms paid, as well as information about the victims. We release a prototype of BitIodine as a library for building Bitcoin forensic analysis tools.
Blockchain analysis systems parse and analyze raw transaction data for many applications. Recently, proposed BlockSci @cite_18 , an open-source, scalable blockchain analysis system that supports various blockchains and analysis tasks. BlockSci incorporates an in-memory, analytical database, which makes it several hundred times faster than its contenders. While there is a minimal support for tagging in its programming interface, BlockSci is designed for analysis of core blockchain data. At the cost of performance, annotation and tagging can be integrated into the analysis pipeline through a centralized, transactional database. For example, proposed BitIodine @cite_17 , an open-source blockchain analysis system that supports tagging through address labels. However, BitIodine, relies on Neo4j @cite_28 , a general-purpose graph database that is not designed for blockchain data and its append-only nature, which makes it inefficient for common blockchain analysis tasks, such as address linking. In contrast, BlockTag is the first open-source tagging system that fills this role.