4PM: Servicio de Concordancia Privada entre Paciente y Proveedor en Sistemas de Salud Digital

For digital health platforms, the challenge is balancing patient privacy with the ability to match patients to the right providers quickly and accurately. Existing systems often suffer from privacy leakage, insufficient matching precision, and degraded performance when dealing with large-scale data. In this paper, we propose 4PM, a novel privacy-preserving patient-provider matching scheme that leverages secure computation to deliver strong privacy guarantees while ensuring efficient and accurate matching. Our method partitions patient data between two non-colluding servers via secret sharing, employing the optimized Millionaires' Protocol for secure ranking and leveraging oblivious retrieval techniques for privacy-preserving matching. 4PM significantly reduces the computational complexity of high-dimensional data, achieving end-to-end latency within 0.5 seconds in scenarios with 200 doctors and 200-dimensional symptom vectors. Our work contributes to fostering secure and trustworthy healthcare in the digital era.