RT Journal
A1 Goldenberg, Meir
A1 Mualem, Loay
A1 Shahar, Amit
A1 Snir, Sagi
A1 Akavia, Adi
T1 Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption
JF Genome Research 
JO Genome Research 
YR 2024 
FD September 01 
VO 34 
IS 9 
SP 1324 
OP 1333 
DO 10.1101/gr.279071.124 
UL http://genome.cshlp.org/content/34/9/1324.abstract 
AB DNA methylation data play a crucial role in estimating chronological age in mammals, offering real-time insights into an individual's 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. A privacy-preserving approach for EPM computation utilizing fully homomorphic encryption was recently introduced. However, this method has limitations, including having high communication complexity and being impractical for large data sets. The current 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 noncolluding servers in prior work). Using techniques from symbolic algebra and number theory, the new protocol eliminates the need for communication during secure computation, significantly improves asymptotic runtime, and offers better compatibility to parallel computing for further time complexity reduction. We implemented our protocol, demonstrating its ability to produce results similar to the standard (insecure) EPM model with substantial performance improvement compared to prior work. 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 in other applications employing similar expectation–maximization techniques.