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Author Topic: How can you trace mixed coins?  (Read 164 times)
BlackHatCoiner (OP)
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November 19, 2021, 12:48:51 PM
Merited by pooya87 (2), ABCbits (1), witcher_sense (1)
 #1

I know that transactions are indissolubly connected from the coinbase reward to their yet unspent transaction outputs. Once you spend an amount, then the change can be easily traced. If you use CoinJoin, you just make the result harder to be concluded, but still; with the analysis one may do, you may get yourself traced.

What I don't understand is how can you trace a person who has mixed the coins in such way that there's no connection between their last UTXOs and the outputs they wanted to mix. It sounds impossible or realistically impractical given that there may be thousands of people who mix coins.

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November 19, 2021, 02:11:46 PM
 #2

If I were the FBI, or the CIA, I'd just secretly run the world's most popular mixers.  That way I'd usually know who is mixing, what they sent into the mixers, and where those funds were headed.
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November 19, 2021, 02:13:08 PM
 #3

If I were the FBI, or the CIA, I'd just secretly run the world's most popular mixers.

But, how can you prove I mixed if you don't reveal this very information?

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November 19, 2021, 02:18:36 PM
Merited by ABCbits (1)
 #4

If I were the FBI, or the CIA, I'd just secretly run the world's most popular mixers.

But, how can you prove I mixed if you don't reveal this very information?

First, I wouldn't need to prove you mixed, I could just use the information that I gained about your mixing to inform additional avenues of investigation where I will find proof of whatever it is that I'm investigating.

Second, I could claim to have used sophisticated Machine Learning software to analyze the mixing transactions.  Since I'm secretly running the mixer, I'll actually know the exact path transaction-to-transaction that the funds took. I can use this knowledge to quickly and easily create separate software that is designed to find that specific path pattern, and then use that analysis as "proof" that the mixing happened and that the source is connected to the target.

Note: This is all "the easy way".

The more complicated way would be to actually train a machine learning algorithm with huge amounts of processing power to search out transaction patterns and assign probabilities that transactions are connected. Then use the highest probabilities to inform additional investigations.
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November 19, 2021, 02:28:08 PM
 #5

So, there's no way for an individual to trace mixed coins other than by owning the mixer. However, there are ways to exclude transactions so they can be closer to the answer, which in most cases can be used to inform additional investigations.

Yeah, that's what I thought.

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November 19, 2021, 03:11:53 PM
 #6

So, there's no way for an individual to trace mixed coins other than by owning the mixer.

Depends on how well the mixer does the mixing.  The authors of some mixing software might be rather lazy or lack the expertise to mix the coins well enough to make it truly difficult to trace.
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November 19, 2021, 03:53:01 PM
 #7

Quote
how can you trace a person who has mixed the coins in such way that there's no connection between their last UTXOs and the outputs they wanted to mix
By coin amounts. For example, ChipMixer uses 0.001 BTC and multiplications of that, so you can trace that by "round amount heuristic": https://en.bitcoin.it/wiki/Privacy#Round_numbers (also you can find many other privacy-related techniques on this page).

Hold your horses before deploying blockchain-related things. You don't want to deploy SHA-1 collision without deploying hardened SHA-1. Once you reveal some code, and make it Open Source, there is no "undo" button. Once you share some idea, there is no way to erase it from reader's memory.
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November 19, 2021, 05:21:27 PM
 #8

By coin amounts. For example, ChipMixer uses 0.001 BTC and multiplications of that, so you can trace that by "round amount heuristic": https://en.bitcoin.it/wiki/Privacy#Round_numbers (also you can find many other privacy-related techniques on this page).

Again, that's exclusion of transactions. You can't know which chip I've received. The fact that you may know I've used ChipMixer doesn't reveal much.

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November 19, 2021, 06:00:02 PM
Merited by BlackHatCoiner (2), ABCbits (1), khaled0111 (1), witcher_sense (1)
 #9

Second, I could claim to have used sophisticated Machine Learning software to analyze the mixing transactions. 
I don't think any of the major blockchain analysis firms use ML.

In order to train a ML model, you need a dataset that is labeled with what you are trying to predict. Since all mixers are different, you would probably need a different model for each mixer. So the dataset would need to be specific for that mixer. If you already had that dataset, why would you need to train a model? You would already have the ability to get the information a model might be able to tell you.

I suspect that blockchain analysis companies are "customers" of various mixers, which will allow them to "watch" where their deposits "go", to draw conclusions about which UTXOs are associated with that particular mixer, which will allow them to deduct when a transaction is going from a third party to the mixer and when a transaction is going from the mixer to a third party. They usually wont know this is real time, but may be able to conclude this information after so many days/weeks.

Blockchain analysis is generally the art of making incremental conclusions and building on those conclusions. Say for example you have a CJ transaction with the following inputs and outputs:
IN | OUT
0.1 | 0.7
0.5 | 0.3
1.0 | 0.33
      | 0.16
      | 0.01
      | 0.06
      | 0.04

The first output can only belong to the third input, and since no other combination of outputs add up to the remaining 0.3 from the fist input, the 2nd input must also be associated with the third input. The only two outputs that add up 0.1, the last two outputs must be associated with the first input. That leaves the middle three outputs being associated with the middle input. This is a rather simple example, and sometimes blockchain analysis might not be able to match all inputs with all outputs, but might learn more information as outputs are spent.

What I don't understand is how can you trace a person who has mixed the coins in such way that there's no connection between their last UTXOs and the outputs they wanted to mix. It sounds impossible or realistically impractical given that there may be thousands of people who mix coins.
See this thread. That person was able to "break" most mixers using a procedure similar to the above two examples in my post.
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November 19, 2021, 06:21:17 PM
Last edit: November 19, 2021, 06:36:31 PM by witcher_sense
Merited by ChipMixer (5), ABCbits (1)
 #10

If you really interested in this topic these research papers might be very helpful.

[PDF]Automatic Bitcoin Address Clustering
https://www.researchgate.net/publication/322670022_Automatic_Bitcoin_Address_Clustering/link/5ab338feaca272171001ceba/download

[PDF]btctrackr : Finding and Displaying Clusters in Bitcoin
https://www.cs.princeton.edu/~arvindn/teaching/spring-2014-privacy-technologies/btctrackr.pdf

[PDF]Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain
Using Supervised Machine Learning
https://core.ac.uk/download/pdf/143481278.pdf

[PDF]Unsupervised Clustering of Bitcoin Transaction Data
https://www2.atmos.umd.edu/~ide/data/teaching/amsc663/14fall/amsc663_14proposalpresentation_stefan_poikonen.pdf

[PDF]Bitcoin Blockchain Analysis
https://is.muni.cz/th/v2dsl/bp_petkanic.pdf

[PDF]Deanonymization and linkability of cryptocurrency transactions based on network analysis
https://orbilu.uni.lu/bitstream/10993/39724/1/biryukov-tikhomirov-deanonymization-and-linkability.pdf

[PDF]BITSCOPE: Scaling Bitcoin Address De-anonymization using Multi-Resolution Clustering
https://izgzhen.github.io/bitscope-public/paper.pdf

[PDF]Evaluating User Privacy in Bitcoin
http://book.itep.ru/depository/bitcoin/User_privacy_in_bitcoin.pdf

[PDF]User Categorization and Community Detection in Bitcoin Network
http://snap.stanford.edu/class/cs224w-2017/projects/cs224w-62-final.pdf

[PDF]Unsupervised Learning for Robust Bitcoin Fraud Detection
https://digifors.cs.up.ac.za/issa/2016/Proceedings/Full/paper%2074.pdf

[PDF]Applying the ETL Process to Blockchain Data. Prospect and Findings
https://res.mdpi.com/d_attachment/information/information-11-00204/article_deploy/information-11-00204.pdf

[PDF]Bitcoin Privacy - A Survey on Mixing Techniques
https://www.semanticscholar.org/paper/Bitcoin-Privacy-A-Survey-on-Mixing-Techniques-Ghesmati-Fdhila/e58962771ca673d384c402d70c4265f407ca3714

[PDF]Everything You Ever Wanted to Know About Bitcoin Mixers
https://keep.lib.asu.edu/_flysystem/fedora/c7/224575/Pakki_asu_0010N_19863.pdf

[PDF]Tracking Mixed Bitcoins
https://www.researchgate.net/profile/Andrea-Eross/publication/344422540_Tracking_Mixed_Bitcoins/links/5ffc279c45851553a036541c/Tracking-Mixed-Bitcoins.pdf

[PDF]An Empirical Analysis of Privacy in the Lightning Network
https://smeiklej.com/files/fc21.pdf

[PDF]WabiSabi: Centrally Coordinated CoinJoins with Variable Amounts
https://www.researchgate.net/profile/Istvan-Seres/publication/349589093_WabiSabi_Centrally_Coordinated_CoinJoins_with_Variable_Amounts/links/60375abca6fdcc37a84e2ab5/WabiSabi-Centrally-Coordinated-CoinJoins-with-Variable-Amounts.pdf

[PDF]Towards Understanding and Demystifying Bitcoin Mixing Services
https://yajin.org/papers/www21_mixing.pdf

[PDF]Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs
https://arxiv.org/pdf/2001.05233

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