Privacy-preserving ridge regression on distributed data
YR Chen, A Rezapour, WG Tzeng - Information Sciences, 2018 - Elsevier
Ridge regression is a statistical method for modeling a linear relationship between a
dependent variable and some explanatory values. It is a building-block that plays a major …
dependent variable and some explanatory values. It is a building-block that plays a major …
Privacy-preserving ridge regression with only linearly-homomorphic encryption
Linear regression with 2-norm regularization (ie, ridge regression) is an important statistical
technique that models the relationship between some explanatory values and an outcome …
technique that models the relationship between some explanatory values and an outcome …
Privacy-preserving distributed linear regression on high-dimensional data
We propose privacy-preserving protocols for computing linear regression models, in the
setting where the training dataset is vertically distributed among several parties. Our main …
setting where the training dataset is vertically distributed among several parties. Our main …
Privacy-preserving ridge regression on hundreds of millions of records
Ridge regression is an algorithm that takes as input a large number of data points and finds
the best-fit linear curve through these points. The algorithm is a building block for many …
the best-fit linear curve through these points. The algorithm is a building block for many …
Fast, privacy preserving linear regression over distributed datasets based on pre-distributed data
M Cock, R Dowsley, ACA Nascimento… - Proceedings of the 8th …, 2015 - dl.acm.org
This work proposes a protocol for performing linear regression over a dataset that is
distributed over multiple parties. The parties will jointly compute a linear regression model …
distributed over multiple parties. The parties will jointly compute a linear regression model …
Privacy-preserving linear regression on distributed data by homomorphic encryption and data masking
G Qiu, X Gui, Y Zhao - IEEE Access, 2020 - ieeexplore.ieee.org
Linear regression is a basic method that models the relationship between an outcome value
and some explanatory values using a linear function. Traditionally, this method is conducted …
and some explanatory values using a linear function. Traditionally, this method is conducted …
PrivFL: Practical privacy-preserving federated regressions on high-dimensional data over mobile networks
Federated Learning (FL) enables a large number of users to jointly learn a shared machine
learning (ML) model, coordinated by a centralized server, where the data is distributed …
learning (ML) model, coordinated by a centralized server, where the data is distributed …
Practical privacy-preserving Gaussian process regression via secret sharing
Gaussian process regression (GPR) is a non-parametric model that has been used in many
real-world applications that involve sensitive personal data (eg, healthcare, finance, etc.) …
real-world applications that involve sensitive personal data (eg, healthcare, finance, etc.) …
Eastfly: Efficient and secure ternary federated learning
Privacy-preserving machine learning allows multiple parties to perform distributed data
analytics while guaranteeing individual privacy. In this area, researchers have proposed …
analytics while guaranteeing individual privacy. In this area, researchers have proposed …
Federated boosted decision trees with differential privacy
There is great demand for scalable, secure, and efficient privacy-preserving machine
learning models that can be trained over distributed data. While deep learning models …
learning models that can be trained over distributed data. While deep learning models …