CAHE-AML: Context-Aware Hybrid Encryption Framework for Secure Cross-Border AML Data Sharing in Cryptocurrency Ecosystems

The growing complexity of cryptocurrency ecosystems has created substantial difficulties for anti-money laundering (AML) enforcement across jurisdictions. Existing AML information-sharing mechanisms are fragmented, slow, and constrained by privacy regulations. This paper presents CAHE-AML, a risk-adaptive architecture built on a context-aware hybrid encryption framework designed to enable secure and privacy-preserving cross-border AML data exchange between regulatory authorities and virtual asset service providers (VASPs). CAHE-AML integrates symmetric encryption, attribute-based encryption (ABE), a decentralized key governance model, and heuristic risk-scoring mechanisms for dynamic policy adaptation. Using an Ethereum Fraud Dataset, we compute risk scores to dynamically assign context-aware access policies across three hierarchical tiers: local, regional, and global. Experimental evaluation on an enterprise-grade multi-core system demonstrates high computational efficiency, achieving substantial throughput and low processing latency. The system identifies integrity breaches with high accuracy while maintaining a manageable data expansion ratio. These results highlight the computational feasibility of CAHE-AML’s cryptographic layer for real-time, high-frequency AML data processing in decentralized financial systems.