A Systematic Literature Review On Privacy Of Deep Learning Systems
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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.
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