Privacy-Enhancing Overlays in Bitcoin

Meiklejohn, Sarah; Orlandi, Claudio

doi:10.1007/978-3-662-48051-9_10

Cited by 85 publications

(53 citation statements)

References 13 publications

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“…Meiklejohn and Orlandi [32] present a definitional framework of anonymity focusing on the ownership of the coin. There are also studies that show experimental evidence on the lack of anonymity in the Bitcoin network.…”

Section: Classification Of the Relevant Papersmentioning

confidence: 99%

Where Is Current Research on Blockchain Technology?—A Systematic Review

et al. 2016

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Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80% of the papers is on Bitcoin system and less than 20% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers.

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Section: Classification Of the Relevant Papersmentioning

confidence: 99%

Where Is Current Research on Blockchain Technology?—A Systematic Review

et al. 2016

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“…Here we describe our algorithm for identifying JoinMarket transactions, and evaluate its effectiveness. We adapt the algorithms from several previous works [34], [61]- [64]. i ← i + 1 25: for all P in PARTITIONS(V ) do 26: t ← 0 27: for all S i in P do 28: if S i ≥ B i then 29: t ← t + 1 30: if t ≥ p then 31: return True 32:…”

Section: Appendixmentioning

confidence: 99%

When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies

Goldfeder

Kalodner

Reisman³

et al. 2018

Proceedings on Privacy Enhancing Technologies

118

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We show how third-party web trackers can deanonymize users of cryptocurrencies. We present two distinct but complementary attacks. On most shopping websites, third party trackers receive information about user purchases for purposes of advertising and analytics. We show that, if the user pays using a cryptocurrency, trackers typically possess enough information about the purchase to uniquely identify the transaction on the blockchain, link it to the user's cookie, and further to the user's real identity. Our second attack shows that if the tracker is able to link two purchases of the same user to the blockchain in this manner, it can identify the user's entire cluster of addresses and transactions on the blockchain, even if the user employs blockchain anonymity techniques such as CoinJoin. The attacks are passive and hence can be retroactively applied to past purchases. We discuss several mitigations, but none are perfect.

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“…Some of the research works explored in-depth investigation on Bitcoin's technological workings, showing its technological flaws and consequent suggestions on how to address them [20], a protocol that enables anonymous payments in Bitcoin and other currencies, which relies on technology commonly used by mixing services [21]. In this regard, an important research contribution is on building an alternative to Bitcoin named Zerocash with zero-knowledge proofs [22] and also privacy-enhancing overlays in Bitcoin from a theoretical perspective [23].…”

Section: Related Workmentioning

confidence: 99%

Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning

Harlev¹,

Yin²,

Langenheldt³

et al. 2018

Proceedings of the Annual Hawaii International Conference on System Sciences

124

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Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity's real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilised a sample of 434 entities (with ≈ 200 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 10 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm, we achieve an accuracy of 77% and F1-score of ≈ 0.75. We discuss our novel approach of Supervised Machine Learning for uncovering Bitcoin Blockchain anonymity and its potential applications to forensics and financial compliance and its societal implications, outline study limitations and propose future research directions.

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Privacy-Enhancing Overlays in Bitcoin

Cited by 85 publications

References 13 publications

Where Is Current Research on Blockchain Technology?—A Systematic Review

Where Is Current Research on Blockchain Technology?—A Systematic Review

When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies

Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning

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