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：深海神经网络(DNN) 深海计算中的推论,通常称为“边缘情报”,要求找到解决办法,确保敏感数据保密和知识产权在此过程中不被披露。隐私保护边缘情报仅出现,尽管作为机器学习服务背景下的“边缘电子”日益普遍。解决方案尚未应用,可能的话也有待调整,以适应最新DNN。本立场文件最初评估了保护隐私DN推断现有技术与“Edge计算”结构特性的兼容性,强调了在这方面秘密共享的适当性。然后我们讨论了模式压缩方法在研究中的未来作用,以秘密分享DNN和最新业绩。