论文标题
深度学习系统隐私的系统文献综述
A Systematic Literature Review On Privacy Of Deep Learning Systems
论文作者
论文摘要
在过去的十年中，深度学习的兴起，其应用在不同领域范围内。但是通常，用于驱动这些系统的数据集包含高度机密和敏感的数据。但是，深度学习模型可以被盗，也可以进行反向工程，可以推断出机密的培训数据，并确定了其他隐私和安全问题。因此，这些系统非常容易受到安全攻击。这项研究强调了学术研究，该研究强调了几种类型的安全攻击，并概述了最广泛使用的隐私解决方案。这种相关的系统评估还阐明了隐私和深度学习领域的研究，教学和用法的潜在可能性。
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.
