DP-ANN: A new Differential Private Artificial Neural Network with Application on Health data (Workshop Paper)

Privacy of the individual data, especially in the Health data, is very sensitive and important. Privacy preserving Machine learning is emerging as one of the solutions for the security of data with the utility to create knowledge. In this paper, we have proposed a differential private artificial neural network (DP-ANN) and shows its application to predict the spread and the peak number of COVID-19 cases. We proposed a differential private artificial neural network (DP-ANN) in which laplacian noise has been introduced at activation function level and it has been compared with existing privacy ideas at error function and weights level of ANN. Results show that DP-ANN model with the private activation function produces the result similar to the base ANN model. © 2020 IEEE.

Spread Peak Prediction of Covid-19 using ANN and Regression (Workshop Paper)

Covid-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, has presented tough times for countries all over the world with number of cases and casualties running in millions. While virologists and doctors have spent sleepless nights to come up with a potent vaccine, the work life of government personnel including administrative staffs, hospital employees etc. has not been any easier. Amidst this turmoil, the common question crossing every mind is concerned with the statistics about this infection including expected number of infections, peak prediction etc. We try to answer these questions by analyzing the time series data of Covid-19 infections for certain hard-hit countries and states in India. A series of machine and deep learning models have been built to capture the infection distribution so that these models could predict the fate of this infection in the near future. We also make an attempt to predict the time when active cases would cease to increase. © 2020 IEEE.