Lightweight Privacy-Preserving Health Data Aggregation for Epidemic Surveillance

Frequent outbreaks of infectious diseases such as COVID-19 and Ebola have highlighted the urgent need for secure, real-time health data management systems capable of supporting coordinated epidemic responses. This paper presents a lightweight cryptographic framework tailored for secure data sharing and privacy-preserving aggregation in healthcare networks. Leveraging Elliptic Curve Digital Signature Algorithm (ECDSA), Elliptic Curve Integrated Encryption Scheme (ECIES), and Paillier Homomorphic Encryption, the proposed system ensures data confidentiality, authenticity, and integrity across key stakeholders, including Laboratories (LABs), City Health Centers (CHCs), and Ministries of Health and Treatment (MHT). The architecture supports secure multi-party communication, dynamic key management, and scalable spatio-temporal analytics, making it suitable for both low-bandwidth environments and high-demand scenarios. Comparative performance analysis demonstrates that the framework outperforms traditional blockchain-based solutions in terms of throughput, latency, scalability, and energy efficiency. This work offers a robust foundation for developing future-ready, secure epidemic surveillance systems that can be effectively deployed in both developed and resource-limited settings.