Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

Abstract

DNA methylation data plays a crucial role in estimating chronological age in mammals, offering real-time insights into an individuals aging process. The Epigenetic Pacemaker (EPM) model allows inference of the biological age as deviations from the population trend. Given the sensitivity of this data, it is essential to safeguard both inputs and outputs of the EPM model. In a recent study by Goldenberg et al., a privacy-preserving approach for EPM computation was introduced, utilizing Fully Homomorphic Encryption (FHE). However, their method had limitations, including having high communication complexity and being impractical for large datasets Our work presents a new privacy preserving protocol for EPM computation, analytically improving both privacy and complexity. Notably, we employ a single server for the secure computation phase while ensuring privacy even in the event of server corruption (compared to requiring two non-colluding servers in Goldenberg et al.). Using techniques from symbolic algebra and number theory, the new protocol eliminates the need for communication during secure computation, significantly improves asymptotic runtime and and offers better compatibility to parallel computing for further time complexity reduction. We have implemented our protocol, demonstrating its ability to produce results similar to the standard (insecure) EPM model with substantial performance improvement compared to Goldenberg et al. These findings hold promise for enhancing data security in medical applications where personal privacy is paramount. The generality of both the new approach and the EPM, suggests that this protocol may be useful to other uses employing similar expectation maximization techniques.