Lightweight verifiable privacy-preserving infectious disease surveillance data aggregation scheme with fault tolerance

With frequent outbreaks of various epidemic infectious diseases across the globe, infectious disease surveillance plays a vital role in stopping the spread of infectious diseases. Privacy-preserving data aggregation is often used to avoid user privacy leakage caused by the transmission of infectious disease data. However, existing data aggregation schemes still have some security problems, such as untrusted aggregation nodes. To solve above problems, we propose a lightweight verifiable privacy-preserving infectious disease surveillance data aggregation scheme with fault tolerance. First, the improved Paillier homomorphic algorithm based on CRT and the signature algorithm with batch verification are used to efficiently encrypt and sign the infectious disease data to protect the data privacy and data integrity during data transmission. Second, the commitment mechanism is used to solve the problem of untrustworthiness of aggregate nodes. In addition, this scheme supports fault tolerance, and the aggregation work can continue even if some users and aggregation nodes do not upload data on time. In particular, this scheme can resist collusion attacks and meet higher security requirements. Since this scheme does not use time-consuming computational operations, such as bilinear mapping, simulation experiments show that the proposed scheme has excellent computational and communication overhead and can be safely and effectively applied to infectious disease surveillance systems.