On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence
It tackles the problem of ensuring data confidentiality and intellectual property protection in edge intelligence for users in edge computing, but it is incremental as it reviews and adapts existing methods.
This position paper assesses the compatibility of existing privacy-preserving techniques for deep neural network inference in edge computing, highlighting secret sharing as appropriate and addressing the future role of model compression methods.
Deep Neural Network (DNN) Inference in Edge Computing, often called Edge Intelligence, requires solutions to insure that sensitive data confidentiality and intellectual property are not revealed in the process. Privacy-preserving Edge Intelligence is only emerging, despite the growing prevalence of Edge Computing as a context of Machine-Learning-as-a-Service. Solutions are yet to be applied, and possibly adapted, to state-of-the-art DNNs. This position paper provides an original assessment of the compatibility of existing techniques for privacy-preserving DNN Inference with the characteristics of an Edge Computing setup, highlighting the appropriateness of secret sharing in this context. We then address the future role of model compression methods in the research towards secret sharing on DNNs with state-of-the-art performance.