Quantum Disease Surveillance Algorithm Based on Private Set Intersection

ABSTRACT

Disease surveillance is vital for public health security, allowing early outbreak detection, reducing transmission, safeguarding population health, and aiding emergency response. This paper proposes a quantum disease surveillance algorithm based on private set intersection (PSI). Specifically, we use the BHT algorithm to solve the PSI problem and improve computational efficiency through candidate subset preprocessing and quantum parallel search mechanisms. It achieves a polynomial speedup over previous algorithms by reducing the communication complexity from to and optimizing the round complexity to a constant, thereby substantially reducing the data transmission overhead. Simulation experiments on the IBM quantum platform verify the correctness and feasibility of our algorithm. Security analysis shows that our algorithm effectively resists both insider and outsider attacks while satisfying privacy‐preservation requirements in disease surveillance scenarios, specifically protecting the privacy of individual clients and the server.