4 papers
End-to-End Differential Privacy in Training Deep Neural Network Classifiers
Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti +1
Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from the parameters of the result…
Differential Privacy for Symbolic Trajectories via the Permute-and-Flip Mechanism
Alexander Benvenuti, Huaiyuan Rao, Matthew Hale
Privacy techniques have been developed for data-driven systems, but systems with non-numeric data cannot use typical noise-adding techniques. Therefore, we develop a new mechanism…
Generating Differentially Private Networks with a Modified Erdős-Rényi Model
Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti +1
Differential privacy has been used to privately calculate numerous network properties, but existing approaches often require the development of a new privacy mechanism for each pro…
Predicting Chaotic System Behavior using Machine Learning Techniques
Huaiyuan Rao, Yichen Zhao, Qiang Lai
Recently, machine learning techniques, particularly deep learning, have demonstrated superior performance over traditional time series forecasting methods across various applicatio…