Abstract
The Epigenetic Pacemaker (EPM) model uses DNA methylation data to predict human epigenetic age. The methylation values are collected from different individuals and are considered to be of medical importance. Sharing this data publicly among labs and other third parties for model calculation purposes may violate the privacy of personal medical records. The use of standard encryption approaches can prevent the exposure of these personal records to third parties, when at rest, but running computations on the data requires decrypting it first, and thus exposing the data to the computing party. This work proposes computing EPM while limiting data exposure by employing cryptographic secure computing techniques including homomorphic encryption. Our protocol has rigorous privacy guarantees against computationally bounded adversaries in the two-server model. We implemented a relaxed version of the protocol showing good correlation with low accuracy error between the model computed with and without encryption.
This work was supported in part by the Israel Science Foundation grant 3380/19 and 3291/21, and by the Israel National Cyber Directorate via the Haifa, BIU and Tel-Aviv Cyber Centers.
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Notes
- 1.
All measurements are for a known and identical set of genome sites.
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Goldenberg, M., Snir, S., Akavia, A. (2022). Private Epigenetic PaceMaker Detector Using Homomorphic Encryption - Extended Abstract. In: Bansal, M.S., Cai, Z., Mangul, S. (eds) Bioinformatics Research and Applications. ISBRA 2022. Lecture Notes in Computer Science(), vol 13760. Springer, Cham. https://doi.org/10.1007/978-3-031-23198-8_6
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