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Membership Inference Against DNA Methylation Databases

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conference contribution
posted on 2023-11-29, 18:13 authored by Inken Hagestedt, Mathias Humbert, Pascal Berrang, Irina Lehmann, Roland Eils, Michael BackesMichael Backes, Yang ZhangYang Zhang
Biomedical data sharing is one of the key elements fostering the advancement of biomedical research but poses severe risks towards the privacy of individuals contributing their data, as already demonstrated for genomic data. In this paper, we study whether and to which extent DNA methylation data, one of the most important epigenetic elements regulating human health, is prone to membership inference attacks, a critical type of attack that reveals an individual’s participation in a given database. We design and evaluate three different attacks exploiting published summary statistics, among which one is based on machine learning and another is exploiting the dependencies between genome and methylation data. Our extensive evaluation on six datasets containing a diverse set of tissues and diseases collected from more than 1,300 individuals in total shows that such membership inference attacks are effective, even when the target’s methylation profile is not accessible. It further shows that the machine-learning approach outperforms the statistical attacks, and that learned models are transferable across different datasets.

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Preferred Citation

Inken Hagestedt, Mathias Humbert, Pascal Berrang, Irina Lehmann, Roland Eils, Michael Backes and Yang Zhang. Membership Inference Against DNA Methylation Databases. In: IEEE European Symposium on Security and Privacy (EuroS&P). 2020.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

IEEE European Symposium on Security and Privacy (EuroS&P)

Legacy Posted Date

2020-06-08

Open Access Type

  • Unknown

BibTeX

@inproceedings{cispa_all_3106, title = "Membership Inference Against DNA Methylation Databases", author = "Hagestedt, Inken and Humbert, Mathias and Berrang, Pascal and Lehmann, Irina and Eils, Roland and Backes, Michael and Zhang, Yang", booktitle="{IEEE European Symposium on Security and Privacy (EuroS&P)}", year="2020", }

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