  The last decade has seen a rise of Deep Learning with its applications
ranging across diverse domains. But usually, the datasets used to drive these
systems contain data which is highly confidential and sensitive. Though, Deep
Learning models can be stolen, or reverse engineered, confidential training
data can be inferred, and other privacy and security concerns have been
identified. Therefore, these systems are highly prone to security attacks. This
study highlights academic research that highlights the several types of
security attacks and provides a comprehensive overview of the most widely used
privacy-preserving solutions. This relevant systematic evaluation also
illuminates potential future possibilities for study, instruction, and usage in
the fields of privacy and deep learning.
