Privacy-Preserving SVM Classification on Vertically Partitioned Data

By Hwanjo Yu, Jaideep Vaidya, Xiaoqian Jiang. 2006

232 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Federated Machine Learning (2019, background): “Secure Support VectorMachines algorithms are developed for vertically partitioned data [73] and horizontally partitioned data [74].”
Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, background): “Questions of privacy-preserving data mining and private computation of machine learning algorithms have been considered in several works [19, 40, 48, 71], providing theoretical protocols without implementations or practical evaluations of their efficiency.”
Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data (2018, background): “Although privacy issue has been considered for these verticallypartitioned datasets (Yunhong, Liang, and Guoping 2009; Yu, Vaidya, and Jiang 2006; Mangasarian, Wild, and Fung 2008), it has rarely been studied with the more restrict differential privacy notion.”
VERTIcal Grid lOgistic regression (VERTIGO) (2016, methodology): “Inspired by the work by Yu et al 15 and Minka, 16 we formulated a strategy to train a LR model using vertical partitions efficiently by solving a dual optimization problem.”
ABY3: A Mixed Protocol Framework for Machine Learning (2018, methodology): “Earlier work on privacy preserving machine learning considered decision trees [39], k-means clustering [15, 35], SVM classification [56, 58], linear regression [25, 26, 49] and logistic regression [52].”
A Privacy Preserving Probabilistic Neural Network for Horizontally Partitioned Databases (2007, unknown): “means clustering [8], Support Vector Machines [9]), and K-”
Secure Linear Regression on Vertically Partitioned Datasets (2016, background): “Questions of privacy preserving data mining and private computation of machine learning algorithms including linear regression have been considered in several works [12, 14, 23, 27, 38, 41] which offer theoretical protocols without implementation and practical evaluations of their efficiency.”
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 [88, 201, 89, 136, 202].”
Preserving privacy between features in distributed estimation (2017, background): “Few approaches have been considered for privacy preserving learning in the distributed setting when the data is partitioned vertically (Yu et al., 2006; Mangasarian et al., 2008; Wu et al., 2012).”
Privacy-preserving SVM classification (2008, unknown): “Reference [48] tackles the problem when the data is vertically partitioned.”
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data (2006, background): “Yu and Vaidya [ 37 ] propose a privacy preserving SVM on vertically partitioned data.”
Vertical Federated Learning: Concepts, Advances, and Challenges (2022, methodology): “The local model Gk can take various forms including tree [19], linear and logistic regression (LR) [3, 18, 20, 21, 22, 23], support vector machine [24, 25], neural network (NN) [26, 27, 28], as well as K-means [29] and EM algorithm [30] etc.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, background): “The Scrub System uses local knowledge sources which compete with one another based on the certainty of their ﬁndings.”
Communication-Efficient Edge AI: Algorithms and Systems (2020, methodology): “[124] considered the dual problem of SVM and adopted a random perturbation strategy, which is suitable only for nodes belong to more than three parties.”