Paper 2019/1305
Privacy-Preserving Computation over Genetic Data: HLA Matching and so on
Jinming Cui, Huaping Li, and Meng Yang
Abstract
Genetic data is an indispensable part of big data, promoting the advancement of life science and biomedicine. Yet, highly private genetic data also brings concerns about privacy risks in data shar- ing. In our work, we adopt the cryptographic prim- itive Secure Function Evaluation (SFE) to address this problem. A secure SFE scheme allows insti- tutions and hospitals to compute a function while preserving the privacy of their input data, and each participant knows nothing but their own input and the final result. In our work, we present privacy-preserving solutions for Human Leukocyte Antigen (HLA) matching and two popular biostatistics tests: Chi-squared test and odds ratio test. We also show that our protocols are compatible with multiple databases simultaneously and could feasibly han- dle larger-scale data up to genome-wide level. This approach may serve as a new way to jointly analyze distributed and restricted genetic data among insti- tutions and hospitals. Meanwhile, it can potentially be extended to other genetic analysis algorithms, allowing individuals to analyze their own genomes without endangering data privacy.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published elsewhere. 1st Federated Machine Learning workshop (2019) in conjection with IJCAI2019
- Keywords
- MPCmultiparty computation
- Contact author(s)
-
cuijinming @ genomics cn
jamie cui @ outlook com - History
- 2019-11-11: received
- Short URL
- https://ia.cr/2019/1305
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2019/1305,
author = {Jinming Cui and Huaping Li and Meng Yang},
title = {Privacy-Preserving Computation over Genetic Data: {HLA} Matching and so on},
howpublished = {Cryptology {ePrint} Archive, Paper 2019/1305},
year = {2019},
url = {https://eprint.iacr.org/2019/1305}
}
