Privacy-Preserving Medical Image Classification Through Secure Neural Inference and Communication-Efficient Computation

Abstract

Medical image classification has become a central application of modern artificial intelligence, yet its clinical value depends on access to sensitive patient data that cannot be freely centralized or exposed. This paper examines privacy-preserving medical image classification as a socio-technical infrastructure problem rather than a narrow cryptographic or machine learning task. It analyzes secure neural inference, communication-efficient federated computation, and the institutional conditions required for trustworthy deployment. The discussion emphasizes structural trade-offs among privacy guarantees, model utility, latency, bandwidth, robustness, fairness, and long-term sustainability. Secure inference architectures can protect patient images during prediction, but their practical viability depends on activation costs, communication rounds, hardware acceleration, and protocol composition. Federated approaches reduce central data pooling but introduce issues of heterogeneity, straggler behavior, poisoning risk, and governance complexity. The paper argues that privacy-preserving medical imaging systems require layered defenses, careful workload partitioning, transparent audit trails, and policy frameworks that assign accountability across hospitals, cloud providers, model developers, and regulators. It also considers cross-domain lessons from finance, critical infrastructure, and public health surveillance. The conclusion is that no single mechanism suffices; instead, resilient medical imaging requires an architecture of complementary protections, measurable service levels, and governance that treats privacy as an operational property rather than a compliance formality. Such an approach can support equitable, robust, and sustainable clinical machine learning without collapsing under communication, trust, or regulatory constraints.