Machine learning with confidential computing: A systematization of knowledge
Privacy and security challenges in Machine Learning (ML) have become increasingly
severe, along with ML's pervasive development and the recent demonstration of large attack …
severe, along with ML's pervasive development and the recent demonstration of large attack …
DeepTrust^ RT: Confidential Deep Neural Inference Meets Real-Time!
Abstract Deep Neural Networks (DNNs) are becoming common in" learning-enabled" time-
critical applications such as autonomous driving and robotics. One approach to protect DNN …
critical applications such as autonomous driving and robotics. One approach to protect DNN …
SecureQNN: Introducing a Privacy-Preserving Framework for QNNs at the Deep Edge
Recent concerns about real-time inference and data privacy are making Machine Learning
(ML) shift to the edge. However, training efficient ML models require large-scale datasets not …
(ML) shift to the edge. However, training efficient ML models require large-scale datasets not …