Privacy-Preserving Statistical Analysis by Exact Logistic Regression
By David duVerle, Shohei Kawasaki, Yoshiji Yamada, Jun Sakuma, Koji Tsuda. 2015
25 citations recorded by the source corpus.
Logistic regression is the method of choice in most genome-wide association studies (GWAS). Due to the heavy cost of performing iterative parameter updates when training such a model, existing methods have prohibitive communication and computational complexities that make them unpractical for real-life usage. We propose a new sampling-based secure protocol to compute exact statistics, that requires a constant number of communication rounds and a much lower number of computations. The publicly available implementation of our protocol (and its many optional optimisations adapted to different security scenarios) can, in a matter of hours, perform statistical testing of over 600 SNP variables across thousands of patients while accounting for potential confounding factors in the clinical data.
Subfields: Genetics, Statistics and Probability
How researchers see this paper
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- Le Trieu Phong
- Yoshinori Aono
- Takuya Hayashi
- Lihua Wang
- Qiang Yang
- Lingjuan Lyu
- Yang Liu
- David Enthoven
Citations in the corpus
- Scalable and Secure Logistic Regression via Homomorphic Encryption (2016, background): “The papers [6,7,18] are also in the setting of secure two-party computation in which each party has a secret training set.”