On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence

• URL: http://arxiv.org/abs/2302.05323v1
• Date: Fri, 10 Feb 2023 15:34:42 GMT
• Title: On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence
• Authors: Daphnee Chabal. Dolly Sapra, Zolt\'an \'Ad\'am Mann
• Abstract summary: 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. This paper provides an original assessment of the compatibility of existing techniques for privacy-preserving DNN Inference with the characteristics of an Edge Computing setup. We then address the future role of model compression methods in the research towards secret sharing on DNNs with state-of-the-art performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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