A non-interactive Online Medical Pre-Diagnosis system on encrypted vertically partitioned data

In medical environments, patient records are stored as heterogeneous features across various institutions, prohibiting raw data sharing due to legal or institutional constraints. This fragmentation presents challenges for Online Medical Pre-Diagnosis (OMPD) systems. Existing methods (such as federated learning) require multiple rounds of interactions among all participating parties (hospitals and cloud servers), resulting in frequent communication. Moreover, due to the sharing of global gradients, they are vulnerable to inference attacks, leading to information leakage. In this paper, we propose a secure and efficient the OMPD system framework to address the problem of vertical data fragmentation, aiming to resolve the contradiction between medical data isolation and model collaboration.

We propose PPNLR, a secure framework for building the OMPD systems. This framework combines functional encryption and blinding factors to design the sample-feature dimension encryption algorithm and the privacy-preserving vectorization training algorithm. Decoupling sample computation from model training enables cross-client data aggregation with only a single communication between hospitals and cloud servers.

Security analysis shows that PPNLR is resistant to semi-honest inference attacks and collusion attacks. Evaluation results based on six real-world medical datasets (text and images) show that: (i) The inference accuracy is close to that of the centralized plaintext training benchmark; (ii) The computational efficiency is at least 3.6× higher than that of comparable approaches; (iii) The communication complexity is significantly reduced by eliminating dependencies on iteration count.

PPNLR achieves data protection through cryptographic primitives, maintaining high diagnostic accuracy while ensuring the security of medical data and model parameters. Its single-communication architecture significantly reduces the deployment threshold in resource-constrained scenarios, providing a practical framework for building the privacy-friendly OMPD systems.