Title |
PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension
|
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Published in |
BMC Medical Genomics, July 2017
|
DOI | 10.1186/s12920-017-0281-2 |
Pubmed ID | |
Authors |
Feng Chen, Chenghong Wang, Wenrui Dai, Xiaoqian Jiang, Noman Mohammed, Md Momin Al Aziz, Md Nazmus Sadat, Cenk Sahinalp, Kristin Lauter, Shuang Wang |
Abstract |
Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data. We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing. We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation. The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 25% |
Unknown | 3 | 75% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 36 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 31% |
Student > Master | 6 | 17% |
Other | 4 | 11% |
Researcher | 4 | 11% |
Student > Doctoral Student | 2 | 6% |
Other | 3 | 8% |
Unknown | 6 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 17 | 47% |
Biochemistry, Genetics and Molecular Biology | 4 | 11% |
Engineering | 3 | 8% |
Nursing and Health Professions | 1 | 3% |
Immunology and Microbiology | 1 | 3% |
Other | 3 | 8% |
Unknown | 7 | 19% |