Analysis of Input-Output Mappings in Coinjoin Transactions with Arbitrary Values
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908602838548480 |
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| author | Gavenda, Jiri Svenda, Petr Bobon, Stanislav Sedlacek, Vladimir |
| author_facet | Gavenda, Jiri Svenda, Petr Bobon, Stanislav Sedlacek, Vladimir |
| contents | A coinjoin protocol aims to increase transactional privacy for Bitcoin and Bitcoin-like blockchains via collaborative transactions, by violating assumptions behind common analysis heuristics. Estimating the resulting privacy gain is a crucial yet unsolved problem due to a range of influencing factors and large computational complexity.
We adapt the BlockSci on-chain analysis software to coinjoin transactions, demonstrating a significant (10-50%) average post-mix anonymity set size decrease for all three major designs with a central coordinator: Whirlpool, Wasabi 1.x, and Wasabi 2.x. The decrease is highest during the first day and negligible after one year from a coinjoin creation.
Moreover, we design a precise, parallelizable privacy estimation method, which takes into account coinjoin fees, implementation-specific limitations and users' post-mix behavior. We evaluate our method in detail on a set of emulated and real-world Wasabi 2.x coinjoins and extrapolate to its largest real-world coinjoins with hundreds of inputs and outputs. We conclude that despite the users' undesirable post-mix behavior, correctly attributing the coins to their owners is still very difficult, even with our improved analysis algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analysis of Input-Output Mappings in Coinjoin Transactions with Arbitrary Values Gavenda, Jiri Svenda, Petr Bobon, Stanislav Sedlacek, Vladimir Cryptography and Security A coinjoin protocol aims to increase transactional privacy for Bitcoin and Bitcoin-like blockchains via collaborative transactions, by violating assumptions behind common analysis heuristics. Estimating the resulting privacy gain is a crucial yet unsolved problem due to a range of influencing factors and large computational complexity. We adapt the BlockSci on-chain analysis software to coinjoin transactions, demonstrating a significant (10-50%) average post-mix anonymity set size decrease for all three major designs with a central coordinator: Whirlpool, Wasabi 1.x, and Wasabi 2.x. The decrease is highest during the first day and negligible after one year from a coinjoin creation. Moreover, we design a precise, parallelizable privacy estimation method, which takes into account coinjoin fees, implementation-specific limitations and users' post-mix behavior. We evaluate our method in detail on a set of emulated and real-world Wasabi 2.x coinjoins and extrapolate to its largest real-world coinjoins with hundreds of inputs and outputs. We conclude that despite the users' undesirable post-mix behavior, correctly attributing the coins to their owners is still very difficult, even with our improved analysis algorithm. |
| title | Analysis of Input-Output Mappings in Coinjoin Transactions with Arbitrary Values |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2510.17284 |