Protecting Individual Identities in Wastewater Data Through Statistical and Differential Privacy Analysis

Wastewater-based epidemiology (WBE) has emerged as a valuable tool for monitoring public health trends and detecting infectious diseases. While traditional WBE assays are focused on detecting and measuring metabolites from illicit drugs and other substances, advances in genetic material screening from wastewater samples enable the recovery of genetic biomarkers, such as human leukocyte antigens (HLA). The increasing deployment of genetic screening technologies in WBE raises significant privacy concerns. In this work, we present a comprehensive statistical analysis of the privacy risks associated with HLA-based wastewater data, focusing on its potential for individual re-identification. We introduce a mathematical framework for estimating the probability as a function of catchment (population) size that an individual can be identified from targeted sequencing of HLA genes in wastewater samples. We then propose a strategy using differential privacy techniques to mitigate these risks which maintains the utility of the data for epidemiological analysis. Our approach demonstrates that, with appropriate addition of noise and data aggregation, individual identities can be protected without compromising the overall effectiveness of wastewater surveillance programs. This work underscores the need for privacy-aware data handling practices in the evolving field of wastewater epidemiology.