Pith. sign in

REVIEW 2 cited by

Heuristics for Detecting CoinJoin Transactions on the Bitcoin Blockchain

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2311.12491 v1 pith:P2EXWKPV submitted 2023-11-21 cs.CR cs.DCcs.LGq-fin.GN

classification cs.CRcs.DCcs.LGq-fin.GN
keywords transactionsblockchaincoinjoinbitcointransactionanalysisheuristicsdelves
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

This research delves into the intricacies of Bitcoin, a decentralized peer-to-peer network, and its associated blockchain, which records all transactions since its inception. While this ensures integrity and transparency, the transparent nature of Bitcoin potentially compromises users' privacy rights. To address this concern, users have adopted CoinJoin, a method that amalgamates multiple transaction intents into a single, larger transaction to bolster transactional privacy. This process complicates individual transaction tracing and disrupts many established blockchain analysis heuristics. Despite its significance, limited research has been conducted on identifying CoinJoin transactions. Particularly noteworthy are varied CoinJoin implementations such as JoinMarket, Wasabi, and Whirlpool, each presenting distinct challenges due to their unique transaction structures. This study delves deeply into the open-source implementations of these protocols, aiming to develop refined heuristics for identifying their transactions on the blockchain. Our exhaustive analysis covers transactions up to block 760,000, offering a comprehensive insight into CoinJoin transactions and their implications for Bitcoin blockchain analysis.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Reliable Is the Multi-Input Heuristic for Bitcoin Address Clustering in Law Enforcement Contexts?

    cs.CR 2026-07 conditional novelty 7.0 of 10

    The multi-input heuristic for Bitcoin address clustering shows metric- and entity-dependent reliability, with dataset-level scores masking near-complete failures for individual services when evaluated against verified...

  2. Bitcoin Research with a Transaction Graph Dataset

    cs.LG 2024-11 conditional novelty 6.0 of 10

    A new public Bitcoin transaction graph dataset with 252M nodes, 785M edges, and ~34K labeled entities, plus GNN baselines.

Pith tools