ES Data Aggregation Scheme based on Personalized Local Differential Privacy

Abstract

In modern society, with the rapid development of technology, research on Epidemiological Survey Data (ESD) has become increasingly crucial. As various diseases continue to evolve and spread, there is a growing need for a profound understanding of the patterns and trends of disease transmission. Epidemiological investigation, as a key method, provides fundamental data support for devising effective prevention and control strategies by tracking cases, contacts, and potential infectees. However, accompanying this progress is the issue of dealing with a large amount of privacy-sensitive data. Once these data are compromised, it may pose severe harm to individuals and society. To address this challenge, we propose a uOUE-based epidemiological survey data aggregation scheme for the collection and processing of ESD, aiming to enhance the efficiency and accuracy of data coding. We ensure the secure, efficient, and accurate aggregation processing of ESD. Through rigorous validation and comparative analysis, our algorithm complies with the requirements of local differential privacy and unbiased estimation. It demonstrates good practicality and accuracy in ESD collection, providing users with a reliable privacy protection effect.