Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916767573475328 |
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| author | Liao, Gordon Y. Zeng, Ziming Belenkiy, Mira Hirshman, Jacob |
| author_facet | Liao, Gordon Y. Zeng, Ziming Belenkiy, Mira Hirshman, Jacob |
| contents | This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities (EAIs)", wallets with direct transaction connections to centralized exchanges. Recognizing the limitations of traditional approaches like blocklisting (reactive, slow) and strict allow listing (privacy-invasive, adoption barriers), we propose a system that analyzes transaction patterns to identify wallets with close connections to centralized exchanges.
Our directed graph analysis of the Ethereum blockchain reveals that 56% of large USDC wallets (with a lifetime maximum balance greater than \$10,000) are EAI and 88% are within one transaction hop of an EAI. For transactions exceeding \$2,000, 91% involve at least one EAI. Crucially, an analysis of past exploits shows that 83% of the known exploiter addresses are not EAIs, with 21% being more than five hops away from any regulated exchange. We present three implementation approaches with varying gas cost and privacy tradeoffs, demonstrating that EAI-based access control can potentially prevent most of these incidents while preserving blockchain openness. Importantly, our approach does not restrict access or share personally identifiable information, but it provides information for protocols to implement their own validation or risk scoring systems based on specific needs. This middle-ground solution enables programmatic compliance while maintaining the core values of open blockchain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention Liao, Gordon Y. Zeng, Ziming Belenkiy, Mira Hirshman, Jacob Cryptography and Security Computational Engineering, Finance, and Science General Economics Economics H.3.5; K.4.4; H.2.8 This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities (EAIs)", wallets with direct transaction connections to centralized exchanges. Recognizing the limitations of traditional approaches like blocklisting (reactive, slow) and strict allow listing (privacy-invasive, adoption barriers), we propose a system that analyzes transaction patterns to identify wallets with close connections to centralized exchanges. Our directed graph analysis of the Ethereum blockchain reveals that 56% of large USDC wallets (with a lifetime maximum balance greater than \$10,000) are EAI and 88% are within one transaction hop of an EAI. For transactions exceeding \$2,000, 91% involve at least one EAI. Crucially, an analysis of past exploits shows that 83% of the known exploiter addresses are not EAIs, with 21% being more than five hops away from any regulated exchange. We present three implementation approaches with varying gas cost and privacy tradeoffs, demonstrating that EAI-based access control can potentially prevent most of these incidents while preserving blockchain openness. Importantly, our approach does not restrict access or share personally identifiable information, but it provides information for protocols to implement their own validation or risk scoring systems based on specific needs. This middle-ground solution enables programmatic compliance while maintaining the core values of open blockchain. |
| title | Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention |
| topic | Cryptography and Security Computational Engineering, Finance, and Science General Economics Economics H.3.5; K.4.4; H.2.8 |
| url | https://arxiv.org/abs/2505.24284 |