Cryptology ePrint Archive: Report 2019/1305

Privacy-Preserving Computation over Genetic Data: HLA Matching and so on

Jinming Cui and 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.

Category / Keywords: applications / MPC, multiparty computation

Original Publication (in the same form): 1st Federated Machine Learning workshop (2019) in conjection with IJCAI2019

Date: received 11 Nov 2019

Contact author: cuijinming at genomics cn, jamie cui@outlook com

Available format(s): PDF | BibTeX Citation

Version: 20191111:210832 (All versions of this report)

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