One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data
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...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2016
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| Online Access: | http://hdl.handle.net/1721.1/105582 https://orcid.org/0000-0002-1537-4000 |
| _version_ | 1811089200185868288 |
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| author | Berger, Bonnie A. Simmons, Sean Kenneth |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Berger, Bonnie A. Simmons, Sean Kenneth |
| author_sort | Berger, Bonnie A. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:15:25Z |
| format | Article |
| id | mit-1721.1/105582 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:15:25Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1055822022-09-28T19:33:14Z One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data Berger, Bonnie A. Simmons, Sean Kenneth Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Mathematics Berger, Bonnie Berger, Bonnie A. Simmons, Sean Kenneth 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. Wellcome Trust (London, England) (Award 076113) 2016-12-05T19:25:41Z 2016-12-05T19:25:41Z 2015-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-9933-0 http://hdl.handle.net/1721.1/105582 Simmons, Sean, and Bonnie Berger. “One Size Doesn’t Fit All: Measuring Individual Privacy in Aggregate Genomic Data.” IEEE, 2015. 41–49. https://orcid.org/0000-0002-1537-4000 en_US http://dx.doi.org/10.1109/SPW.2015.25 2015 IEEE Security and Privacy Workshops Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Prof. Berger via Michael Noga |
| spellingShingle | Berger, Bonnie A. Simmons, Sean Kenneth One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data |
| title | One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data |
| title_full | One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data |
| title_fullStr | One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data |
| title_full_unstemmed | One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data |
| title_short | One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data |
| title_sort | one size doesn t fit all measuring individual privacy in aggregate genomic data |
| url | http://hdl.handle.net/1721.1/105582 https://orcid.org/0000-0002-1537-4000 |
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