One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data
Name
PrivMAF.pdf
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371.47 KB
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Adobe PDF
Checksum (MD5)
759aa213f10f2bb7ec7a30918a860ce4
Author(s)
Berger, Bonnie A.
Simmons, Sean Kenneth
Date Issued
May 2015
Journal
2015 IEEE Security and Privacy Workshops
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Simmons, Sean, and Bonnie Berger. “One Size Doesn’t Fit All: Measuring Individual Privacy in Aggregate Genomic Data.” IEEE, 2015. 41–49.
Version
Author's final manuscript
Abstract
Even in the aggregate, genomic data can reveal sensitive information about individuals. We present a new model-based measure, PrivMAF, that provides provable privacy guarantees for aggregate data (namely minor allele frequencies) obtained from genomic studies. Unlike many previous measures that have been designed to measure the total privacy lost by all participants in a study, PrivMAF gives an individual privacy measure for each participant in the study, not just an average measure. These individual measures can then be combined to measure the worst case privacy loss in the study. Our measure also allows us to quantify the privacy gains achieved by perturbing the data, either by adding noise or binning. Our findings demonstrate that both perturbation approaches offer significant privacy gains. Moreover, we see that these privacy gains can be achieved while minimizing perturbation (and thus maximizing the utility) relative to stricter notions of privacy, such as differential privacy. We test PrivMAF using genotype data from the Welcome Trust Case Control Consortium, providing a more nuanced understanding of the privacy risks involved in an actual genome-wide association studies. Interestingly, our analysis demonstrates that the privacy implications of releasing MAFs from a study can differ greatly from individual to individual. An implementation of our method is available at http://privmaf.csail.mit.edu.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/SPW.2015.25
