PropertyValue
?:abstract
  • 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
is ?:annotates of
?:creator
?:journal
  • Proc._-_IEEE_Int._Conf._Multimed._Big_Data,_BigMM
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • DP-ANN: A new Differential Private Artificial Neural Network with Application on Health data (Workshop Paper)
?:type
?:who_covidence_id
  • #972165
?:year
  • 2020

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