Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?. (arXiv:2205.00436v2 [cs.LG] UPDATED)

This paper investigates the problem of forecasting multivariate aggregated
human mobility while preserving the privacy of the individuals concerned.
Differential privacy, a state-of-the-art formal notion, has been used as the
privacy guarantee in two different and independent steps when training deep
learning models. On one hand, we considered \textit{gradient perturbation},
which uses the differentially private stochastic gradient descent algorithm to
guarantee the privacy of each time series sample in the learning stage. On the
other hand, we considered \textit{input perturbation}, …

deep learning human input lg mobility