A survey of machine learning-based solutions to protect privacy in the Internet of Things
Abstract The Internet of things (IoT) aims to connect everything and everyone around the
world to provide diverse applications that improve quality of life. In this technology, the …
world to provide diverse applications that improve quality of life. In this technology, the …
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Consider two data providers, each maintaining private records of different feature sets about
common entities. They aim to learn a linear model jointly in a federated setting, namely, data …
common entities. They aim to learn a linear model jointly in a federated setting, namely, data …
[HTML][HTML] Logistic regression model training based on the approximate homomorphic encryption
Background Security concerns have been raised since big data became a prominent tool in
data analysis. For instance, many machine learning algorithms aim to generate prediction …
data analysis. For instance, many machine learning algorithms aim to generate prediction …
A review of secure federated learning: Privacy leakage threats, protection technologies, challenges and future directions
L Ge, H Li, X Wang, Z Wang - Neurocomputing, 2023 - Elsevier
Advances in the new generation of Internet of Things (IoT) technology are propelling the
growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption …
growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption …
When homomorphic encryption marries secret sharing: Secure large-scale sparse logistic regression and applications in risk control
Logistic Regression (LR) is the most widely used machine learning model in industry for its
efficiency, robustness, and interpretability. Due to the problem of data isolation and the …
efficiency, robustness, and interpretability. Due to the problem of data isolation and the …
Entity resolution and federated learning get a federated resolution
Consider two data providers, each maintaining records of different feature sets about
common entities. They aim to learn a linear model over the whole set of features. This …
common entities. They aim to learn a linear model over the whole set of features. This …
[HTML][HTML] Privacy-preserving logistic regression training
C Bonte, F Vercauteren - BMC medical genomics, 2018 - Springer
Background Logistic regression is a popular technique used in machine learning to
construct classification models. Since the construction of such models is based on …
construct classification models. Since the construction of such models is based on …
[HTML][HTML] High performance logistic regression for privacy-preserving genome analysis
M De Cock, R Dowsley, ACA Nascimento… - BMC Medical …, 2021 - Springer
Background In biomedical applications, valuable data is often split between owners who
cannot openly share the data because of privacy regulations and concerns. Training …
cannot openly share the data because of privacy regulations and concerns. Training …
Secure and differentially private logistic regression for horizontally distributed data
Scientific collaborations benefit from sharing information and data from distributed sources,
but protecting privacy is a major concern. Researchers, funders, and the public in general …
but protecting privacy is a major concern. Researchers, funders, and the public in general …
Cloud-based quadratic optimization with partially homomorphic encryption
This article develops a cloud-based protocol for a constrained quadratic optimization
problem involving multiple parties, each holding private data. The protocol is based on the …
problem involving multiple parties, each holding private data. The protocol is based on the …