  In this paper, an adjustment to the original differentially private
stochastic gradient descent (DPSGD) algorithm for deep learning models is
proposed. As a matter of motivation, to date, almost no state-of-the-art
machine learning algorithm hires the existing privacy protecting components due
to otherwise serious compromise in their utility despite the vital necessity.
The idea in this study is natural and interpretable, contributing to improve
the utility with respect to the state-of-the-art. Another property of the
proposed technique is its simplicity which makes it again more natural and also
more appropriate for real world and specially commercial applications. The
intuition is to trim and balance out wild individual discrepancies for privacy
reasons, and at the same time, to preserve relative individual differences for
seeking performance. The idea proposed here can also be applied to the
recurrent neural networks (RNN) to solve the gradient exploding problem. The
algorithm is applied to benchmark datasets MNIST and CIFAR-10 for a
classification task and the utility measure is calculated. The results
outperformed the original work.
