Title |
FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption
|
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Published in |
BMC Medical Informatics and Decision Making, December 2015
|
DOI | 10.1186/1472-6947-15-s5-s5 |
Pubmed ID | |
Authors |
Yuchen Zhang, Wenrui Dai, Xiaoqian Jiang, Hongkai Xiong, Shuang Wang |
Abstract |
The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment. We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics. The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets. Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 2% |
Unknown | 42 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 26% |
Researcher | 5 | 12% |
Professor > Associate Professor | 4 | 9% |
Student > Master | 4 | 9% |
Other | 3 | 7% |
Other | 6 | 14% |
Unknown | 10 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 14 | 33% |
Medicine and Dentistry | 4 | 9% |
Biochemistry, Genetics and Molecular Biology | 3 | 7% |
Agricultural and Biological Sciences | 2 | 5% |
Nursing and Health Professions | 1 | 2% |
Other | 4 | 9% |
Unknown | 15 | 35% |