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Tracing Monero to Catch a Hacker

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A report from Finland’s National Bureau of Investigation has revealed that they were able to track Monero (XMR) transactions to identify a ransomware hacker, despite the privacy coin’s supposed anonymous features.

The investigation involves Julius Aleksanteri Kivimäki, who is on trial for allegedly hacking into a mental health firm’s database in October 2022 and demanding a ransom of 40 Bitcoin (BTC). When the ransom was not paid, Kivimäki targeted individual patients and received crypto payments that were then converted into Monero.

The hacker then reportedly sent the funds to an exchange without KYC, swapped them into Monero, then transferred the funds to Binance and back into BTC across various wallets. Authorities have stated that they were able to successfully track the transactions on-chain, despite Monero’s reputation for being untraceable due to privacy technologies like RingCT, ring signatures, and stealth addresses.

This case highlights the fact that crypto forensics can follow the money trail even with anonymity features, challenging the notion of complete anonymity provided by privacy coins like Monero. Additionally, past research has also found that Monero transactions were traceable before 2017, prompting a re-evaluation of the privacy and anonymity provided by these types of cryptocurrencies.

The trial of Kivimäki also sheds light on the enforcement capabilities to de-anonymize illicit activities on blockchains, indicating that law enforcement agencies are becoming increasingly adept at tracking and identifying individuals involved in illegal activities using cryptocurrencies.

Overall, this case serves as a reminder that even with the supposed anonymity provided by privacy coins like Monero, law enforcement agencies are able to track and identify individuals involved in criminal activities using blockchain forensics. It also raises questions about the level of privacy and anonymity provided by such cryptocurrencies, and whether they truly offer the level of protection that users believe they do.

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