Multi-Party Private Edge Computing for Collaborative Quantitative Exposure Detection of Endemic Diseases

Facing the global threat of endemic diseases, utilizing edge computing for exposure detection enables efficient monitoring of the dynamic distribution of infected patient groups across regions, enhancing the management and control of these diseases. Employing the quantitative exposure detection of endemic diseases, regions seek to reconcile patient information collected through mobile devices, aiming to obtain statistical and analytical results based on the intersection of patient lists. In this paper, we propose a privacy-preserving scheme for the collaborative quantitative exposure detection of endemic diseases, which ensures each region to only learn the statistical results, without any information about other regions' datasets. Our scheme is fundamentally achieved through Circuit-based Private Set Intersection (Circuit-PSI) that can compute functions over the set intersection without disclosing the intersection itself. However, the state-of-the-art solution involves a laborious process in which one party iteratively compares its elements with those of others, which leads to a significantly high communication complexity. Therefore, we introduce a novel multi-party protocol that can diminish the communication overhead of circuit-PSI through a skillful decoupling of the comparison complexity from the number of parties. To achieve this, we design a multiparty oblivious encoding scheme, which can prevent any party from inferring any private info through the encoded data. By filtering out the repeated elements, the comparison complexity is independent of the number of parties. Furthermore, to address scenarios involving patient information with additional attributes, we extend our protocol to include payloads by developing a lightweight multiparty data mapping algorithm. Our extensive experiments show that compared to prior works, our protocol achieves a substantial reduction in communication overhead by 6.4×, and runs 1.2× faster in the LAN setting and 3.1× in the WAN setting.