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Home » Bitcoin mixer operator convicted due to poor blockchain forensics
Bitcoin mixer operator convicted due to poor blockchain forensics
Bitcoin mixer operator convicted due to poor blockchain forensics

Bitcoin mixer operator convicted due to poor blockchain forensics

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By admin on 2024-03-12 Regulations Security

I found myself seated at the counsel table beside Roman Sterlingov, a 35-year-old man, during the largest ever trial for Bitcoin-mixer money laundering. Throughout the proceedings, we referred to him as “Mr. Sterlingov,” but I simply knew him as Roman. He was the defendant, and we anxiously awaited the jury’s verdict.

On March 12, the verdict was delivered: “guilty.” The word echoed four times as each of the four counts of the indictment were read aloud. It felt as though I had been struck in the gut. To cope with the news, my brain immediately shifted its focus to formulating strategies for an appeal.

The trial lasted a grueling four weeks. I spent an entire day on the stand, and I had been working on this case for a year. The case revolved around Bitcoin Fog, the largest Bitcoin mixer in the history of BTC. Over its lifespan, Bitcoin Fog processed approximately 1.2 million Bitcoin. Allegedly, hundreds of millions of dollars in drug money from darknet sites like Silk Road and AlphaBay were laundered through this platform. The prosecution claimed that Roman not only used Bitcoin Fog, but also operated it.

Throughout the trial, defense counsel Tor Ekeland and Mike Hassard fought tenaciously, battling against every motion and objection. It reminded me of Paul Newman’s character in “The Verdict,” but with a modern twist involving cryptocurrency technology.

The prosecution’s case closely aligned with the original indictment. It centered around a Bitcoin transaction from Sterlingov’s Mt. Gox account that eventually ended up in a Bitcoin wallet. The owner of that wallet and the holder of its private key remained unknown. From there, a series of transactions were traced back to the purchase of a Bitcoin Fog clearnet site, which provided instructions on how to access Bitcoin Fog on the darknet.

The government emphasized Sterlingov’s usage of Bitcoin Fog. He admitted to regularly using the platform for privacy purposes. The government’s claim was that Sterlingov had sent 2,700 Bitcoin through Bitcoin Fog. However, during my testimony, I explained that the true operator of Bitcoin Fog would have earned between 24,000 and 36,000 Bitcoin based on the platform’s fees. This would amount to hundreds of millions of dollars, similar to what a government witness, Larry Harmon, testified to earning while running the related Bitcoin mixer Helix. However, the government’s IRS witness presented evidence showing that Roman never spent more than $60,000 per year, lived in a modest one-bedroom apartment, and was never worth more than $1.8 million during the ten years they monitored him.

The “Perry Mason” moment of the trial occurred when defense expert Jeff Fishbach made a crucial discovery. The government’s evidence included a screenshot of a text message chain discussing a money laundering plan attributed to the defendant. It turned out that the screenshot was simply a picture from an e-book that Roman was reading on his computer. The prosecution apologized for their mistake during closing arguments but assured the jury it was their only error.

Interestingly, the prosecutors, C. Alden Pelker and Chris Brown, had previously advised against building cases solely based on tracing. They recommended using corroborating evidence, such as possession of a private key to Bitcoin addresses holding illicit funds. This advice was sound, as academic literature has shown that Chainalysis heuristics, which were relied upon in this case, can be incorrect 90% of the time. Relying solely on such methods is not a solid foundation for a case that could result in decades of imprisonment. Yet, in this instance, the prosecutors made the very mistake they had cautioned against.

One of the key issues was the Chainalysis “co-spend” heuristic, which assumes that Bitcoin spent together originated from the same user. This assumption is flawed when two individuals split a dinner bill using Bitcoin. Another flawed heuristic is the “peel chain,” which assumes that unspent Bitcoins are linked in a chain where the larger transaction represents the spender keeping their “change.” However, this assumption fails when the larger amount is sent to another person within that chain. It also fails when a person simply gives another individual the private key in an off-chain transaction, which was more common during the early years of Bitcoin, the period examined in this case.

These two tracing heuristics formed the basis of Chainalysis’ investigation. The Chainalysis expert acknowledged criticisms of her tools during her testimony and suggested that the source code of Chainalysis contained fixes for these issues. However, she could not share the proprietary code, leaving us with no choice but to trust her claims.

In many ways, Roman was an early adopter of Bitcoin, which made him fortunate. He also valued his privacy and used Bitcoin Fog for that purpose. With his Russian passport and interest in computers, he became an easy target for the authorities to pin the operation of Bitcoin Fog on. In that sense, he may have been the most unfortunate individual I have ever encountered.

J.W. Verret is an associate professor at George Mason University’s Antonin Scalia Law School. He is a practicing crypto forensic accountant and securities lawyer at Lawrence Law LLC. Verret is a member of the Financial Accounting Standards Board’s Advisory Council and a former member of the SEC Investor Advisory Committee. He also leads the Crypto Freedom Lab, a think tank advocating for policy changes to protect freedom and privacy for crypto developers and users.

This article is intended for general information purposes only and should not be construed as legal or investment advice. The opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph.

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