Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data

By Jaideep Vaidya, Chris Clifton. 2004

287 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Federated Machine Learning (2019, background): “Vaidya and Clifton proposed secure association mining rules [65], secure k-means [66], Naive Bayes classifier [64] for vertically partitioned data.”
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data (2008, methodology): “Vaidya and Clifton [27] present a privacypreserving solution for vertically partitioned data.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, background): “It has been shown in [118] that such a system is able to remove more than 99% of the identifying information from the data.”
Privacy-Preserving Backpropagation Neural Network Learning (2009, background): “If there is a learner trusted by all the data holders, then the trusted learner can collect data ﬁrst and build a learning model.”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “propose multiple secure models on vertically partitioned data, including association rule mining [173], k-means [174], naive bayes classifier [175] and decision tree [176].”
A Privacy Preserving Probabilistic Neural Network for Horizontally Partitioned Databases (2007, methodology): “These include a decision tree algorithm [6], Naive Bayes [7], k-”
A framework for high-accuracy privacy-preserving mining (2004, background): “Maintaining input data privacy is considered in [17, 25, 26, 27 ] in the context of databases that are distributed across a number of sites with each site only willing to share data mining results, but not the source data.”
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning (2016, methodology): “Furthermore, the proposed scheme prevents the disclosure of the private using homomorphic encryption which has been successfully used for data mining and knowledge discovery such as decision trees [25], [26], Bayesian networks [27], [28], support vector machines [29], and k-means [30].”
Privacy-preserving SVM classification (2008, unknown): “Specifically , secure methods have been proposed for association rule mining [18, 36], clustering[37, 38, 39], classification [40, 41 , 42, 43], outlier detection [44] and regression[45, 46].”
Privacy-preserving decision trees over vertically partitioned data (2005, background): “This includes Lindell’s work [2], as well as work on association rule mining [10{13], clustering [14, 15], and some work on classication [ 16 , 5]. While some of this work makes trade-os between eciency and information disclosure, all maintain provable privacy of individual information and bounds on”