Privacy-Preserving Computation over Genetic Data: HLA Matching and so on
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 sharing. In our work, we adopt the cryptographic primitive Secure Function Evaluation (SFE) to address this problem. A secure SFE scheme allows institutions 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 privacypreserving 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 handle 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 institutions and hospitals. Meanwhile, it can potentially be extended to other genetic analysis algorithms, allowing individuals to analyze their own genomes without endangering data privacy.