Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.

翻译：作为最强大的机器学习算法之一的深神经网络(DNN)日益被利用来克服有效探索和分析大规模数据以促进先进科学发展的瓶颈。云计算供应商提供基于云的DNN(DNN)作为绝版服务并不奇怪。虽然云基DN(DNN)有一些好处,但云层中两个或多个实体之间的互动机制不可避免地带来新的隐私风险。本调查展示了基于云的神经网络服务中出现的隐私攻击和防御的最新发现。我们系统彻底地审查基于云的 DNN(DN)服务管道中的隐私攻击和防御,即数据操纵、培训和预测。特别是从最近出版的文献中提取了一个新的理论,称为基于云的ML(ML)隐私游戏,以提供对最新科技研究的深入了解。最后,挑战和未来的工作是帮助研究人员继续推进隐私攻击者和维权者之间的竞争。