Abstract
As the amount of personal genomic information and privacy concerns in data publication have been growing, several studies have pointed out that the presence information of a particular individual could be revealed from the statistics obtained in large-scale genomic analyses. Existing methods for releasing genome statistics under differential privacy do not prevent the leakage of personal information by untrusted data collectors. In addition, the existing studies for statistical tests using a contingency table had restrictions on the number of cases and controls. Moreover, the methods for correcting for population stratification cannot protect genotype information. Thus, developing a more general and stronger method is desired. In this study, we present privacy-preserving methods for releasing key genome statistics. Our methods enhance the randomized response technique and guarantee individuals’ privacy, even when untrusted data collectors exist. Moreover, our methods do not require any restrictions on the contingency tables, and they also guarantee the privacy of targeted genotype information for the analyses to correct for population stratification. The experimental results indicate that our methods can achieve comparable high accuracy to existing methods while preserving privacy more strictly from any data collectors. Furthermore, for statistical analysis using a contingency table, we consider the case where different privacy budgets are assigned to each of the row and column information, and present optimal methods in terms of privacy assurance for the entire table that outperform the existing method. Overall, this study is the first step toward genomic statistical analysis under local differential privacy. The Python implementation of our experiments and Supplementary Material are available at https://github.com/ay0408/LDP-genome-statistics.
Supported by JSPS KAKENHI Grant Numbers 20H05967, 21H05052, and 23H03345, and JSPS Grant-in-Aid for JSPS Fellows Grant Number 23KJ0649. The super-computing resource was provided by Human Genome Center, the Institute of Medical Science, the University of Tokyo.
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Yamamoto, A., Shibuya, T. (2023). Privacy-Preserving Genomic Statistical Analysis Under Local Differential Privacy. In: Atluri, V., Ferrara, A.L. (eds) Data and Applications Security and Privacy XXXVII. DBSec 2023. Lecture Notes in Computer Science, vol 13942. Springer, Cham. https://doi.org/10.1007/978-3-031-37586-6_3
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DOI: https://doi.org/10.1007/978-3-031-37586-6_3
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