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.

翻译：深度神经网络（DNN）推理在边缘计算中，通常被称为边缘智能，需要确保敏感数据的机密性和知识产权在此过程中不被泄露。尽管边缘计算作为机器学习即服务（Machine-Learning-as-a-Service）的上下文日益普及，但隐私保护的边缘智能仍处于起步阶段。解决方案尚需应用，并可能适应于先进DNN。本文立场性地对现有隐私保护DNN推理技术与边缘计算环境特征的兼容性进行了原创性评估，突显了秘密共享在此背景下的适用性。随后，我们探讨了模型压缩方法在未来研究中对于实现秘密共享在具有先进性能的DNN上的作用。