Logistic Regression, Neural Networks
Avoids inversion and limits expensive confidential divisions to one.
LMC, Fisher’s LDA
Multiplications with incorporated division factors
Avoids division costs and simplifies the protocol.
Ridge Linear Regression
Reduces the cost complexity by half.
Sorting based matrix factorization
Reduces the overall complexity from quadratic to within a polylogarithmic factor of the complexity in the plaintext
Random Linear Classifiers
Reduced number of comparisons and simplicity in learning.
Avoids costs involved in multiple levels of multiplications.
Eigen-approximation by Lanczos and Nystrom
Reduces complexity of the problem from O(N3) to O(N2).