Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data

225 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.

Federated Machine Learning (2019, background): “Secure Support VectorMachines algorithms are developed for vertically partitioned data [73] and horizontally partitioned data [74].”
Privacy-Preserving Backpropagation Neural Network Learning (2009, methodology): “In particular, privacy preserving solutions have been proposed for the following classiﬁcation algorithms (to name a few): decision trees [12], [17], [18], Naive Bayes classi-ﬁer [19], [20], and SVM [21], [22], [23].”
Privacy-preserving SVM classification (2008, unknown): “[46] presents the complete details for the scalable and secure protocol that computes the kernel matrix in linear time, without disclosing any data.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, unknown): “In this section, we will discuss a number of different applications of privacy-preserving data mining methods.”
Privacy-Preserving SVM Classification on Vertically Partitioned Data (2006, background): “Yu and Vaidya [21] developed a privacy-preserving SVM classification on horizontally partitioned data.”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Applying the above methods, the user privacy of FLSs can usually be well protected [89, 211, 91, 144].”