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

Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey