Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security - Qayyum, Adnan; Ijaz, Aneeqa; Usama, Muhammad; Iqbal, Waleed; Qadir, Junaid; Elkhatib, Yehia; Al-Fuqaha, Ala (Frontiers in Big Data, 2020)
With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party cloud services for outsourcing training of DL models, which requires substantial costly computational resources (e.g., high-performance graphics processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a wide range of attack surfaces for adversaries to exploit the ML/DL system to achieve malicious goals. In this paper, we conduct a systematic evaluation of literature of cloud-hosted ML/DL models along both the important dimensions—attacks and defenses—related to their security. Our systematic review identified a total of 31 related papers out of which 19 focused on attack, 6 focused on defense, and 6 focused on both attack and defense. Our evaluation reveals that there is an increasing interest from the research community on the perspective of attacking and defending different attacks on MLaaS platforms. In addition, we identify the limitations and pitfalls of the analyzed papers and highlight open research issues that require further investigation.