Building decision tree classifier on private data

By Wenliang Du, Zhijun Zhan. 2002

508 citations recorded by the source corpus.

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Citations in the corpus

Federated Machine Learning (2019, methodology): “Ref [58] proposed a collaboratively deep learning scheme where participants train independently and share only subsets of updates of parameters.”
Privacy-preserving SVM classification (2008, unknown): “Specifically, secure methods have been proposed for association rule mining [18, 39], clustering [17, 22, 35], classification [10, 36, 38, 43], outlier detection [37], and regression [21, 31].”
A Privacy Preserving Probabilistic Neural Network for Horizontally Partitioned Databases (2007, methodology): “These include a decision tree algorithm [6], Naive Bayes [7], k-”
Privacy preserving decision tree learning over multiple parties (2007, methodology): “Du and Zhan [16] use randomization to build approximate decision trees.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “Several measures are used to identify the optimal feature that best splits the training samples, including information gain [127] and the Gini index [128].”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, methodology): “are utilized for identification of the origin node, which optimally divides the training datasets like the Gini index [122] and Information Gain [123].”
State-of-the-art in privacy preserving data mining (2004, background): “The work described in [12] studies the building process of a decision tree classifier for a database that is vertically distributed.”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Cryptographic methods such as homomorphic encryption [15, 77, 27, 33, 74, 145, 146, 204, 206, 112], and secure multi-party computation (SMC) [157, 36, 23, 51, 24, 101, 17, 62, 91, 181, 39, 65] are widely used in privacy-preserving machine learning algorithms.”
Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data (2004, background): “An ID3 classifier on vertically partitioned data is presented in [5].”
Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, background): “Horizontal distributions refer to the cases where different records of the same data attributes are resided in different places [7] - [11] [26] ; while in a vertical data distribution, different attributes of the same record of data are resided in different places [2] [23] [24] [25] [30] [33] .”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, background): “Recently, there has been a growing body of research on secure inner product computation [43], [44], [45], [46], which looks similar with ours.”
Privacy-preserving collaborative data mining (2007, methodology): “Thus, according to Protocol 1, Alice gets Ux[1], Ux[2], Ux[3], Ux[4] and Ux[5] and Bob gets Uy[1], Uy[2], Uy[3], Uy[4] and Uy[5].”
Privacy-preserving Naïve Bayes classification (2008, background): “While [27] showed how to generate ID3 decision trees on horizontally partitioned data,acompletelynewmethodwasneededforvertically partitioned data [ 10 ].”
A survey of multiple classifier systems as hybrid systems (2014, background): “Data partitions They may be compelled by several reasons, such as data privacy, or the need to learn over distributed data chunks stored in different databases [80–82].”
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data (2006, background): “Prior work has also addressed association rule mining [31, 16], clustering [19, 28], and classification [7, 29, 33].”