Proceedings of the 59th ACM/IEEE Design Automation Conference · 2022 · 11 citations · 9 references
ComputingCloud ComputingComputer ScienceArtificial IntelligenceCloud SecurityMachine LearningData ProtectionDeep LearningNeural NetworksPattern RecognitionPrivacy-preserving AnalyticsSecure Multi-party ComputationComputational SecurityPrivacy-preserving ComputationPrivacy-preserving LearningPrivacy-preserving Machine LearningPrivacy-preserving Model TrainingSecure Machine Learning
Data privacy and security issues are preventing a lot of potential on-cloud machine learning as services from happening. In the recent past, secure multi-party computation (MPC) has been used to achieve the secure neural network predictions, guaranteeing the privacy of data. However, the cost of the existing two-party solutions is expensive and they are impractical in real-world setting.
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SecureML: A System for Scalable Privacy-Preserving Machine Learning
Payman Mohassel, Yupeng Zhang · 2017 · 1.8K citations
Oblivious Neural Network Predictions via MiniONN Transformations
Jian Liu, Mika Juuti, Yao Lu et al. · 2017 · 700 citations
A Proof of Security of Yao’s Protocol for Two-Party Computation
Yehuda Lindell, Benny Pinkas · Journal of Cryptology · 2008 · 655 citations · Full text
Payman Mohassel, Peter Rindal · 2018 · 423 citations