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.
The impact of Bitcoin address linking on user anonymity and privacy has been known for a while now @cite_6 @cite_25 @cite_19 @cite_1 . Fergal and Martin @cite_6 showed that passive analysis of public Bitcoin information can lead to a serious information leakage. They constructed two graphs representing transactions and users from Bitcoin's blockchain data and annotated the graphs with auxiliary data, such as user accounts from BitcoinTalk and Twitter. The authors used visual content discovery and flow analysis techniques to investigate Bitcoin theft. Alternatively, @cite_1 explored the level of anonymity in the Bitcoin network. The authors annotated addresses in the transaction graph with user accounts collected from BitcoinTalk in order to show that users can be linked to transactions through their public Bitcoin addresses. These studies show the value of using public data sources for Bitcoin privacy research and law enforcement, which is our goal behind designing BlockTag.
@cite_19 @cite_1 @cite_25 @cite_6
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This paper contributes a systematic account of transaction anonymization techniques that do not require trust in a single entity and support the existing cryptographic currency Bitcoin. It surveys and compares four known techniques, proposes tailored metrics to identify the use of each technique (but not necessarily its users), and presents longitudinal measurements indicating adoption trends and teething troubles. There is a tradeoff between the choice of users' preferred protection mechanisms and the risk that pertaining transactions can be singled out, which hurts privacy due to smaller anonymity sets unless a critical mass adopts the mechanism. Bitcoins have recently become an increasingly popular cryptocurrency through which users trade electronically and more anonymously than via traditional electronic transfers. Bitcoin's design keeps all transactions in a public ledger. The sender and receiver for each transaction are identified only by cryptographic public-key ids. This leads to a common misconception that it inherently provides anonymous use. While Bitcoin's presumed anonymity offers new avenues for commerce, several recent studies raise user-privacy concerns. We explore the level of anonymity in the Bitcoin system. Our approach is two-fold: (i) We annotate the public transaction graph by linking bitcoin public keys to "real" people - either definitively or statistically. (ii) We run the annotated graph through our graph-analysis framework to find and summarize activity of both known and unknown users. Bitcoin has created a new exchange paradigm within which financial transactions can be trusted without an intermediary. This premise of a free decentralized transactional network however requires, in its current implementation, unrestricted access to the ledger for peer-based transaction verification. A number of studies have shown that, in this pseudonymous context, identities can be leaked based on transaction features or off-network information. In this work, we analyze the information revealed by the pattern of transactions in the neighborhood of a given entity transaction. By definition, these features which pertain to an extended network are not directly controllable by the entity, but might enable leakage of information about transacting entities. We define a number of new features relevant to entity characterization on the Bitcoin Blockchain and study their efficacy in practice. We show that even a weak attacker with shallow data mining knowledge is able to leverage these features to characterize the entity properties. Bitcoin is a digital currency which relies on a distributed set of miners to mint coins and on a peer-to-peer network to broadcast transactions. The identities of Bitcoin users are hidden behind pseudonyms (public keys) which are recommended to be changed frequently in order to increase transaction unlinkability. We present an efficient method to deanonymize Bitcoin users, which allows to link user pseudonyms to the IP addresses where the transactions are generated. Our techniques work for the most common and the most challenging scenario when users are behind NATs or firewalls of their ISPs. They allow to link transactions of a user behind a NAT and to distinguish connections and transactions of different users behind the same NAT. We also show that a natural countermeasure of using Tor or other anonymity services can be cut-off by abusing anti-DoS countermeasures of the Bitcoin network. Our attacks require only a few machines and have been experimentally verified. The estimated success rate is between 11 and 60 depending on how stealthy an attacker wants to be. We propose several countermeasures to mitigate these new attacks.