Privacy-preserving decision trees over vertically partitioned data

By Jaideep Vaidya, Chris Clifton, Murat Kantarcioglu, A. Scott Patterson. 2005

310 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

SplitNN-driven Vertical Partitioning (2020, background): “Vertically partitioned decision trees The works in (Gascón et al. , 2016; Vaidya et al. , 2008; Vaidya & Clifton, 2004; Kourtellis et al. , 2016; Cheng et al. , 2019) share approaches for vertically partitioned learning with decision trees, gradient boosted decision trees and Hoeffding trees.”
SecureBoost: A Lossless Federated Learning Framework (2019, background): “Unlike the situation where the data are horizontally split, the above setting requires a more complex mechanism to decompose the loss function at each party (Vaidya 2008; Vaidya and Clifton 2005; Hardy et al. 2017).”
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data (2008, background): “[29] propose a solution that solves a more general problem – constructing an ID3 decision tree when the training data is vertically partitioned between many parties (≥ 2) and the class attribute is known to only a single party.”
Communication-Efficient Edge AI: Algorithms and Systems (2020, methodology): “A privacy-preserving classiﬁcation approach based on decision tree was proposed in [122], which adopts secure multi-party computation procedures including commutative encryption to determine if there are any remaining attributes and secure cardinality computation of set intersection.”
Security and Privacy in the Internet of Things (2017, background): “Most existing work falls into this category, which either use cryptographic techniques [107, 143] or privacy-preserving data mining techniques such as value distortion [26], randomized response [61] and partitioned data [144, 63].”
Privacy-preserving Naïve Bayes classification (2008, background): “There has since been work to address association rule mining [24,42], clustering [21,26,39], classification [10,40,41,43].”
Privacy-preserving outsourced classification in cloud computing (2017, background): “A lot of works have been supposed for privacy-preserving training algorithm, such as back-propagation networks [16, 24], naïve bayes [25,26], K-means clustering [27–29], association rules [30,31], linear regression [32,33], decision trees [31,34] and so forth.”
A comprehensive review on privacy preserving data mining (2015, methodology): “Vaidya et al. (2008) developed an approach for vertically partitioned mining data.”
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 survey on trust management for Internet of Things (2014, background): “It is widely used in privacy preserving collaborative computation: privacy preserving computational geometry [44], privacy preserving statistic analysis [45], privacy preserving matrix computation [46], privacy preserving scientific computation [47], especially privacy preserving data mining [48-52]”
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] .”
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].”