Privacy-Preserving Medical Knowledge Retrieval with Self-Supervised Hash Learning and Adversarial Defense for Healthcare AI Agents
Keywords:
Privacy-preserving retrieval; self-supervised hashing; adversarial defense; healthcare AI agents; medical knowledge graphs; secure multi-party computationAbstract
The rapid integration of artificial intelligence agents into clinical decision support systems has intensified the demand for medical knowledge retrieval pipelines that simultaneously ensure high-fidelity semantic access, rigorous privacy protection, and resilience against adversarial manipulation. This paper presents a system-level architectural investigation into privacy-preserving medical knowledge retrieval that unifies self-supervised hash learning with layered adversarial defense mechanisms. By mapping medical knowledge fragments into compact binary codes through self-supervised contrastive objectives, the framework enables efficient approximate nearest-neighbor search over distributed repositories without exposing sensitive clinical content. The proposed architecture couples a differentially private hash encoder with secure index structures and an adversarial sanitization module that monitors query integrity and defends against both input-space perturbations and knowledge-poisoning attacks. We examine structural trade-offs between hash code length, retrieval precision, computational latency, and achievable privacy guarantees, drawing on cross-domain insights from cryptographically secure computation, federated learning, and adversarial robustness literature. Deployment considerations are analyzed within the context of hospital information ecosystems and cross-border regulatory regimes, addressing governance challenges such as algorithmic auditing, fairness across heterogeneous patient populations, and the sustainability of large-scale hash-based retrieval infrastructure. The study further explores forward-looking policy implications for certifying autonomous healthcare AI agents that rely on privacy-preserving retrieval as a core cognitive operation. The analysis demonstrates that self-supervised hashing constitutes a promising foundation for trust-enhancing knowledge access, yet requires careful co-design with adversarial defense, governance frameworks, and lifecycle management to withstand emerging threat surfaces in medical decision-making environments.
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