Present industry increasing adoption of blockchain technology, blockchain networks have become prime targets for attackers seeking to exploit vulnerabilities in the deployments. To address this challenge, this paper proposes a novel Zero-Knowledge Data-Assisted Elusive Adversary Interface (ZKD-EAI) designed to mitigate large-scale data collection attacks in blockchain ecosystems. The proposed interface integrates Zero-Knowledge Data (ZKD) mechanisms with intelligent deception techniques to safeguard blockchain networks against adversarial reconnaissance and data extraction attempts. By employing zero-knowledge proof (ZKP) principles, the interface ensures that no sensitive information is disclosed during validation or verification, while simultaneously deploying fake yet plausible data responses to mislead attackers. This dual-layer design enhances privacy, integrity, and deception-based defence within blockchain operations. The ZKD-EAI framework is highly adaptive and modular, supporting seamless integration with existing blockchain infrastructures without significant architectural reconfiguration. Experimental evaluations were conducted on a simulated blockchain network incorporating intelligent honeypots, demonstrating that the proposed system effectively 97.8 % reduces data leakage, increases adversary confusion, and strengthens resilience under various attack conditions.