Homomorphic multi-label classification of virus strains

Zhou, Junwei; Lei, Botian; Lang, Huile

doi:10.1109/issrew55968.2022.00082

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…A promising approach for overcoming such limitations is to execute privacy preserving genome analysis, i.e., to utilize cryptographic techniques such as fully homomorphic encryption (FHE) [29,15] that enable processing data in encrypted form, so that even the entity processing it is never exposed to the underlying data in cleartext. Prior work on privacy preserving genome analysis using homomorphic encryption focused primarily on Genome Wide Association (GWAS) [24,32,8,7,12] -i.e., statistically associating innate genome variability in single nucleotide polymorphism (SNPs) with a risk for a disease or a particular trait-as well as on privacy preserving classification of DNA and RNA sequences of tumor tissues [20,10] and viral strains [41,2] respectively. Recently, privacy preserving epigenetics -i.e., the study of how behavior and environmental factors lead to genome changes that in turn affect the phenotype-was considered in [3,16], analysing gene expression data [3] and DNA methylation data [16].Elaborating on the latter, DNA methylation are chemical changes in the genome that are linked to numerous developmental, physiologic, and pathologic processes including malignancy, infections, and aging.…”

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confidence: 99%

Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

Goldenberg,

Mualem,

Shahar

et al. 2024

Preprint

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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 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.

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mentioning

confidence: 99%

Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

Goldenberg,

Mualem,

Shahar

et al. 2024

Preprint

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Privacy Preserving Epigenetic PaceMaker: Stronger Privacy and Improved Efficiency

Goldenberg,

Mualem,

Shahar

et al. 2024

Lecture Notes in Computer Science

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Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption

Goldenberg,

Mualem,

Shahar

et al. 2024

Genome Res.

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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.

show abstract

Homomorphic multi-label classification of virus strains

Cited by 3 publications

References 23 publications

Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

Privacy Preserving Epigenetic PaceMaker: Stronger Privacy and Improved Efficiency

Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption

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