Privacy preserving regression modelling via distributed computation

By Ashish P. Sanil, Alan F. Karr, Xiaodong Lin, Jerome P. Reiter. 2004

159 citations recorded by the source corpus.

Subfields: Artificial Intelligence, ML Systems & Hardware, Privacy & Security

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Citations in the corpus

Federated Machine Learning (2019, neutral): “Privacy-preserving machine-learning algorithms have been proposed for vertically partitioned data, including cooperative statistical analysis [15], association rule mining [65], secure linear regression [22, 32, 55], classification [16], and gradient descent [68].”
Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, background): “Early implementations of privacy-preserving linear regression protocols [21, 65] either don’t use formal threat models, or leak additional information beyond the result of the computation.”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, background): “Previous work mostly focused on data that is partitioned either horizontally or vertically across a few databases [8], [40]–[44].”
Deriving private information from randomized data (2005, methodology): “described a privacy-preserving algorithm of computing regression coefficients [22].”
VERTIcal Grid lOgistic regression (VERTIGO) (2016, unknown): “~ H 1⁄4 k 1 diag y ð Þ K diag y ð Þ þ cI (10)”
Compressed and Privacy-Sensitive Sparse Regression (2009, background): “In other work, the authors in [20] consider the problem of privacy-preserving regression analysis in distributed data, where different variables appear in different databases but it is of interest to integrate data across databases.”
Context-Aware Verifiable Cloud Computing (2017, background): “described an algorithm that enables agencies to compute the exact regression coefficients of the global regression equation and also perform some basic goodness-of-fit diagnostics while protecting the confidentiality of their data [14].”
SecureML: A System for Scalable Privacy-Preserving Machine Learning (2017, unknown): “Earlier work on privacy preserving machine learning has focused on decision trees [31], k-means clustering [28], [14], SVM classification [48], [44], linear regression [19], [20], [40] and logistic regression [42].”
Compressed Regression (2007, methodology): “We refer to [6] for discussion and further pointers into this literature; recent work includes [16].”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, unknown): “[149] present a secure regression model.”
Fast, Privacy Preserving Linear Regression over Distributed Datasets based on Pre-Distributed Data (2015, background): “Most of them clearly do not even aim at obtaining the level of privacy usually required by modern cryptographic protocols (such as Karr et al. [21] and Du et al.[14], see also [29, 22]).”
Privacy-Preserving SVM Classification on Vertically Partitioned Data (2006, background): “In terms of data mining problems, work addressed includes association rule mining [15], clustering [16, 17], classification [18], and regression [19, 20].”
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].”
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].”