The field of blockchain research is moving towards enhancing decentralization and security. Recent studies have focused on developing innovative solutions to prevent fraud, improve consensus mechanisms, and ensure the integrity of blockchain networks. Notably, graph-based approaches have shown promise in detecting and preventing fraudulent activities, such as smurfing and money laundering. Additionally, researchers have explored new methods for decentralized key management, accountable RPC protocols, and private smart wallets. These advancements aim to strike a balance between security, decentralization, and usability, ultimately contributing to the growth and adoption of blockchain technology. Notable papers include:
- Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention, which proposes a novel system for detecting fraudulent transactions using transaction proximity and easily attainable identities.
- GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering, which introduces a graph-based method for quantifying smurfing risk and demonstrates its effectiveness in detecting fraudulent activities.