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PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers
Safeguarding privacy in machine learning is highly desirable, especially...
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Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning
Machine learning has started to be deployed in fields such as healthcare...
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CryptoNN: Training Neural Networks over Encrypted Data
Emerging neural networks based machine learning techniques such as deep ...
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BLAZE: Blazing Fast Privacy-Preserving Machine Learning
Machine learning tools have illustrated their potential in many signific...
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Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
Security monitoring via ubiquitous cameras and their more extended in in...
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Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption
Much of machine learning relies on the use of large amounts of data to t...
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Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation Approach
Knowledge discovery is one of the main goals of Artificial Intelligence....
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PINFER: Privacy-Preserving Inference for Machine Learning
The foreseen growing role of outsourced machine learning services is raising concerns about the privacy of user data. Several technical solutions are being proposed to address the issue. Hardware security modules in cloud data centres appear limited to enterprise customers due to their complexity, while general multi-party computation techniques require a large number of message exchanges. This paper proposes a variety of protocols for privacy-preserving regression and classification that (i) only require additively homomorphic encryption algorithms, (ii) limit interactions to a mere request and response, and (iii) that can be used directly for important machine-learning algorithms such as logistic regression and SVM classification. The basic protocols are then extended and applied to feed-forward neural networks.
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