Fast, Privacy Preserving Linear Regression over Distributed Datasets based on Pre-Distributed Data

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Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Our use of fixed-point encodings follows closely previous works on secure linear regression [10, 35, 59].”
Practical Privacy Preserving POI Recommendation (2020, methodology): “ It has been proven that this truncation technique also works when z is secret shared [40]. Secret sharing has been popolarly used in kinds of machine learning algorithms, including linear regression [15], neural network [40], and recommender system [9]. In this paper, we apply secret sharing into d”
Secure Linear Regression on Vertically Partitioned Datasets (2016, methodology): “Our fixed-point encoding strategy follows closely previous works on secure linear regression [9, 19, 35].”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, background): “Modern MPC protocol implementations are fast enough to securely evaluate complex functions on moderatelylarge data sets, such as the numerous implementations of secure regression analyses with tens to hundreds of thousands of observations, and tens to hundreds of variables [23], [38], [62], [87], [1”
Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Cities (2019, methodology): “different ML models, including SVM [11], logistic regression [12], [13], decision trees [15], and Naive Bayes [14].”