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SecureML: A System for Scalable Privacy-Preserving Machine Learning
2017
2017 IEEE Symposium on Security and Privacy (SP)
Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. In this paper, we present new and efficient protocols for privacy preserving machine learning for linear regression, logistic regression and neural network training using the stochastic
doi:10.1109/sp.2017.12
dblp:conf/sp/MohasselZ17
fatcat:y6lwerwfwnfghjkia76yqenmiy