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）在边缘计算中的推理（通常称为边缘智能）需要解决方案以确保敏感数据的机密性和知识产权在过程中不被泄露。尽管边缘计算作为机器学习即服务的场景日益普及，隐私保护的边缘智能仍处于初步发展阶段。现有解决方案尚需应用于（并可能适配）先进DNN。本文立场性评估了现有隐私保护DNN推理技术与边缘计算场景特征的兼容性，强调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面向先进性能DNN的秘密共享研究中的未来角色。