Privacy Preserving Epigenetic PaceMaker: Stronger Privacy and Improved Efficiency

Meir Goldenberg, Loay Mualem, Amit Shahar, Sagi Snir, Adi Akavia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

DNA methylation data plays 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 epigenetic 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, 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 the secure computing phase, significantly improves asymptotic runtime, 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.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings
EditorsJian Ma
PublisherSpringer Science and Business Media Deutschland GmbH
Pages412-416
Number of pages5
ISBN (Print)9781071639887
DOIs
StatePublished - 2024
Event28th International Conference on Research in Computational Molecular Biology, RECOMB 2024 - Cambridge, United States
Duration: 29 Apr 20242 May 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14758 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on Research in Computational Molecular Biology, RECOMB 2024
Country/TerritoryUnited States
CityCambridge
Period29/04/242/05/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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