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Found 5 papers, 1 papers with code
Quantifying (Hyper) Parameter Leakage in Machine Learning
While the attacks proposed in literature are empirical, there is a need for a theoretical framework to measure the information leaked under such extraction attacks.
Fault Tolerance of Neural Networks in Adversarial Settings
Specifically, this work studies the impact of the fault tolerance of the Neural Network on training the model by adding noise to the input (Adversarial Robustness) and noise to the gradients (Differential Privacy).
Towards Enhancing Fault Tolerance in Neural Networks
In the view of difference in functionality, a Neural Network is modelled as two separate networks, i. e, the Feature Extractor with unsupervised learning objective and the Classifier with a supervised learning objective.
Stealing Neural Networks via Timing Side Channels
Deep learning is gaining importance in many applications.
Fuzzy Graph Modelling of Anonymous Networks
Anonymous networks have enabled secure and anonymous communication between the users and service providers while maintaining their anonymity and privacy.
Cryptography and Security Networking and Internet Architecture
