Private Computation on Encrypted Genomic Data

195 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Crypto-Nets: Neural Networks over Encrypted Data (2014, methodology): “Using Homomorphic Encryption (HE) to do machine learning and statistical analysis on encrypted data has been investigated in (Bos et al., 2014; Bost et al., 2014; Graepel et al., 2013; Lauter et al., 2014; Nikolaenko et al., 2013a;b; Wu & Haven, 2012).”
Scalable and Secure Logistic Regression via Homomorphic Encryption (2016, unknown): “2 For example, in [14, 17], Θ = (θ∗ 1 , θ ∗ 2) ∈ Z(2) and θ∗ = θ∗ 1/θ 2 ∈ R, as current homomorphic encryption schemes do not support division directly.”
Homomorphic Encryption for Arithmetic of Approximate Numbers (2017, background): “HE can be applied to the evaluation of various algorithms on encrypted financial, medical, or genomic data [11,29,31,36,41].”
Private genome analysis through homomorphic encryption (2015, methodology): “[16] presented a method to encode a person’s genotype given a candidate allele associated to a specified disease.”
Bootstrapping for Approximate Homomorphic Encryption (2018, background): “For example, HE can be a solution to performing the computation of various algorithms on financial, medical, or genomic data without any information leakage [15,36,38,39,41].”
Machine learning and genomics: precision medicine versus patient privacy (2018, background): “(by an authority) if their queries match a predefined allowable query policy [45].”