Privacy and Security Implications of Cloud-Based AI Services : A Survey
Alka Luqman Riya Mahesh Anupam Chattopadhyay
This paper details the privacy and security landscape in today's cloud
ecosystem and identifies that there is a gap in addressing the risks introduced
by machine learning models. As machine learning algorithms continue to evolve
and find applications across diverse domains, the need to categorize and
quantify privacy and security risks becomes increasingly critical. With the
emerging trend of AI-as-a-Service (AIaaS), machine learned AI models (or ML
models) are deployed on the cloud by model providers and used by model
consumers. We first survey the AIaaS landscape to document the various kinds of
liabilities that ML models, especially Deep Neural Networks pose and then
introduce a taxonomy to bridge this gap by holistically examining the risks
that creators and consumers of ML models are exposed to and their known
defences till date. Such a structured approach will be beneficial for ML model
providers to create robust solutions. Likewise, ML model consumers will find it
valuable to evaluate such solutions and understand the implications of their
engagement with such services. The proposed taxonomies provide a foundational
basis for solutions in private, secure and robust ML, paving the way for more
transparent and resilient AI systems.