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    Area of Science:

    • Genomics
    • Bioinformatics
    • Privacy-preserving technologies

    Background:

    • Advancements in DNA sequencing fuel personalized genetic services, increasing the release of sensitive genome information.
    • Individual genome data is highly sensitive and linked to relatives, posing significant privacy risks.
    • Genome-wide association studies (GWAS) link genotypes to phenotypes, creating potential vulnerabilities.

    Purpose of the Study:

    • To develop an inference attack algorithm to predict target genotypes and phenotypes using shared genomic data and GWAS.
    • To propose metrics for evaluating data utility and privacy.
    • To present a data sanitization method to mitigate privacy threats from inference attacks.

    Main Methods:

    • An inference attack algorithm based on belief propagation in factor graphs was developed.
    • Metrics for assessing data utility and privacy were defined.
    • A data sanitization method was proposed and implemented.
    • The methods were evaluated on a real-world Age-related macular degeneration (AMD) GWAS dataset.

    Main Results:

    • The inference attack algorithm effectively predicts target genotypes and phenotypes using shared genomic data and GWAS information.
    • The proposed data sanitization method successfully defends against genome privacy threats.
    • The evaluation demonstrates that the sanitization method preserves essential data utility.

    Conclusions:

    • The developed inference attack poses a significant threat to genome privacy.
    • The proposed data sanitization method offers an effective defense against such privacy attacks.
    • This work balances genome privacy protection with the utility of genetic data for research.