Artificial Intelligence

AI Blockchain Tracking: What It Means for Bitcoin Privacy

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At the core of the Bitcoin network and other cryptocurrencies is a strong tension.

On the one hand, these cryptocurrencies were designed to provide the maximum individual freedom and anonymity.

On the other hand, regulatory compliance, forensic auditing, and cybercrime investigation require attribution of crypto assets to specific people with identified names, addresses, etc.

This tension has been growing in recent years, as Bitcoin became a “mainstream” asset, owned by investment funds, banks, regulated ETFs, etc.

“A key issue for governments is the unauthorized adoption of cryptocurrencies such as Bitcoins for cybercrime, money laundering, and terrorism funding. Contrary to accepted assumptions regarding the robustness of anonymity offered by the Bitcoin blockchain, revealing the genuine identity of the individuals under such circumstances would be morally and legally permissible, although it would prove logically challenging.”

So overall, from its initial more libertarian days, the price of Bitcoin today could benefit from a reduced level of anonymity.

Progress in machine learning and other AI technologies could help “de-anonymize” Bitcoin ownership. But it needs to be reliable enough to be trusted in use cases involving finding out criminal activities and satisfying the demands of financial regulators.

A new study by Indian researchers at the Pranveer Singh Institute of Technology and Manipal University Jaipur looks at how new AI improvements (integrative neural architectures) could help with this task, achieving an accuracy of approximately 97%.

“This research intends to promote the expansion of economic growth without using unethical means. Lawmakers searching for data-driven sources to project the blockchain-based cryptocurrency landscape, businesses looking for compliance and efficient risk assessment on Bitcoin blockchain transactions, and law enforcement institutions wishing to examine and investigate addresses linked to illicit activity will all find value in the findings.”

They published their findings in Scientific Reports1, under the title “Adaptive deanonymization of blockchain transactions leveraging integrative neural architectures in decentralized networks”.

From Addresses To Identities

Finding Patterns

In most efforts to identify Bitcoin addresses’ identities, the focus is on single addresses. Instead, this study exploits DSU-based entity resolution to break down the pseudonymity of the blockchain into long-lasting actor/entity clusters.

Also known as Union-Find or merge-find, Disjoint Set Union (DSU) compares attributes (like name, email, or phone) to determine if two separate records represent the same real-world person or object.

In the context of Bitcoin, this means grouping multiple pseudonymous Bitcoin addresses together into a single “entity cluster” belonging to a single real-world actor.

This is important as users can generate unlimited public addresses, making identification of single addresses not especially useful, at least in the case of sophisticated criminal actors, the very ones for which de-anonymization is most important.

Building The Right Tool

To build an AI tool able to identify Bitcoin users, the researchers built an integrative neural architecture, which combines different neural network paradigms:

  • Convolutional Neural Networks (CNNs) extract local metadata, granular, invariant patterns found within raw transaction metadata that identify specific entity types from the feature vector.
    • This is used to identify the “how”: specific script and fee patterns.
  • Long Short-Term Memory (LSTM) models flow velocity.
    • This is used to identify the “when”: signature and timing intervals of transaction flows.
  • Signature and timing intervals of transaction flows are captured by treating structural maps.
  • Graph Neural Networks (GNNs) aggregate embeddings via a dynamic k-Nearest Neighbor (k-NN) adjacency matrix within batches to incorporate neighborhood behavior.
    • This is used to identify the “who”: topological neighborhood and hub affiliation.

The study tested its method on a dataset of 427,625 transactions and 140,235 unique entities. The transaction history was taken from the archives of WalletExplorer and Blockchair, with each transaction including 22 features like block_id, transaction hash, fee_used, size, timestamp, weight, etc.

The training of model was carried out on Intel(R) Core (TM) I9-10,900 CPU, 2.81 GHz, 32 GB RAM, 8 GB NVIDIA GeForce RTX 3070 GPU.

Using AI To De-Anonymize Bitcoin Transactions

How Does It Work?

To explain how these complex systems work, a step-by-step explanation helps.

The first stage is to create a dataset with all the transactions and have them labeled so they can be analyzed further.

Then start the step of “address clustering”, based on the assumption that when there are several addresses used as the input in a specific transaction, these addresses pertain to a single shared private entity. So this step links together previously separate Bitcoin addresses using the Disjoint Set Union (DSU) algorithm. This step led to an almost 67.2% reduction in noise.

The ledger data is then transformed into a “Directed Graph”, finding the most active users and distinguishing between accumulation addresses (sinks) and distribution addresses (hubs), as well as the relative importance of each cluster within the ecosystem.

The data generated are also categorized according to their four possible function in the Bitcoin network: exchange, mining pools, mixing services, and gambling. Exchange and mining pools are considered by default as legitimate activities.

Finally, the integrative neural architecture (CNN-LSTM-GNN) is used to identify individual clusters.

“The structural maps from CNN are treated as a sequence. The LSTM captures the sequential patterns of transaction activity over time or flow velocity. The GNN models the relational structure and connectivity of addresses and transactions, vital for Bitcoin blockchain since each transaction and address can be naturally represented as a node or edge in a graph.”

High-Quality Results

The CNN→LSTM→GNN model was able to deliver very consistent results across the different evaluation metrics, with deviations within 1–2% in terms of ranking and classification metrics and 1.5% for the error rate.

“The model achieves near-perfect link prediction (AUC ≈ 0.995), exceptionally high-ranking precision (MRR ≈ 0.985), and almost complete retrieval success (Hits@10 ≈ 0.999), while maintaining a very low false positive rate (<3%).”

The model was also very capable of categorizing whether a Bitcoin address is the right type of activity.

“One of the most important achievements of the model is the capability to draw a fine line between mixing services and gambling organizations. The model also detects minority classes such as mining pools which constitute~0.53% of the dataset. Without this, the model would likely ignore them in favor of the majority ‘Exchanges’ class.”

More importantly, it was able to maintain an extremely low false positive rate (<3%), which is very important to avoid any erroneous linking of identities in de-anonymization tasks. This is critical for preventing ‘innocent bystander’ mislabeling in analytical audits.

When checking which layer of the model was most important to the final results by removing some layer and conducting the analysis, the researchers found that, overall, Long Short-Term Memory (LSTM) was the least impactful layer when removed, but still contributed significantly to the final performance, with Convolutional Neural Networks (CNNs) the most important part of the AI model.

Investors Takeaways

While part of the initial value proposition of Bitcoin was anonymity, this study and others make clear that progress in forensic blockchain analysis and AI can quickly identify which Bitcoin addresses belong to the same individual or organization.

In addition, to demonstrate that the framework can be generalized, a thorough cross-dataset evaluation was performed in heterogeneous blockchain environments, including Elliptic, Bitcoin Elliptic+, Bitcoin Heist Ransomware, and Ethereum (ETH ) Fraud Detection, and it still achieved high accuracy consistently, from 90 to 99%.

So this means that Bitcoin’s value is now almost fully no longer linked to anonymity, but instead to its network value as a payment system, its “digital gold” structure (absolute scarcity, immutability, decentralized nature, portability, etc.), and overall the public & financial institutions’ enthusiasm for the digital currency.

Improved analytics from studies like this one could likely increase support for institutional participation. But it could also create new privacy and false-positive concerns, with a lot of caution required around considering an “only” 97% accurate analysis as absolute proof of connection to illegal activities.

Investing In Bitcoin

Coinbase

COIN Price Chart

As cryptocurrencies become more mainstream, more investors are gaining exposure to them or increasing their presence in cryptocurrency markets. The same is true as crypto projects multiply.

This is beneficial to companies like Coinbase, which are closely connected to cryptocurrency trading, asset prices, custody, and institutional participation.

Therefore, investing in crypto exchanges and technology platforms like Coinbase can be a way for investors to get exposure to the crypto market regime, without requiring an arbitrary selection of specific tokens.

However, this does not mean that Coinbase will provide investors in its publicly traded stock the same kind of potential returns (and risks) that direct crypto investment can provide. Instead, it is an investment in crypto-market infrastructure, with Coinbase reporting $246 billion in assets on its platform as of June 30, 2026.

Today, besides Coinbase’s main app and crypto exchange, the company has a series of complementary offerings:

  • Coinbase One, a premium membership service offering zero trading fees, boosted staking rewards, and deals with partners like a crypto tax calculator, crypto research, etc.
  • Coinbase Advanced, for professional crypto traders.
  • Coinbase Wallet, for self-custody of cryptocurrencies outside of exchanges, as well as NFTs.
  • Coinbase Earn, a staking service where cryptocurrency owners can lock their crypto in order to earn interest from the network, with $230M earned by Coinbase’s customers in 2023.
  • Coinbase Card, a Visa debit card to make purchases using cryptocurrencies, with 1% back in Bitcoin when paying with USD, and 1.5% in USDC when paying with ETH. The card is accepted everywhere Visa debit cards are accepted.
  • USD Coin (USDC), a digital stablecoin with a value equal to the US dollar, is looking to create a “digital dollar”.

As more and more advanced tools, including AI, get developed, they will complement existing crypto infrastructure. Now a more mature and dominant platform, Coinbase is well-positioned to capitalize on crypto becoming increasingly mainstream through the growing trends of Bitcoin ETFs, stablecoins, and stock tokenization.

(You can read more about Coinbase’s history, offerings, and investment potential in our dedicated report on the company)

Bitcoin

Another way to get exposure to the Bitcoin network is, of course, direct ownership of Bitcoin.

To get started, you can read our guides on the topic:

Latest Coinbase (COIN) Stock News and Developments

Study Referenced

1. Rohit Saxena, et al.Adaptive deanonymization of blockchain transactions leveraging integrative neural architectures in decentralized networks. Scientific Reports. 15 October 2026. https://doi.org/10.1038/s41598-026-69816-5 

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Jonathan is a former biochemist researcher who worked in genetic analysis and clinical trials. He is now a stock analyst and finance writer with a focus on innovation, market cycles and geopolitics in his publication 'The Eurasian Century".