Current developments in Enterprise Systems observe a paradigm shift, moving the needle from the backend to the edge sectors of those; by distributing data, decentralizing applications and integrating novel components seamlessly to the central systems. Distributively deployed AI capabilities will thrust this transition. Several non-functional requirements arise along with these developments, security being at the center of the discussions. Bearing those requirements in mind, hereby we propose an approach to holistically protect distributed Deep Neural Network (DNN) based/enhanced software assets, i.e. confidentiality of their input & output data streams as well as safeguarding their Intellectual Property. Making use of Fully Homomorphic Encryption (FHE), our approach enables the protection of Distributed Neural Networks, while processing encrypted data. On that respect we evaluate the feasibility of this solution on a Convolutional Neuronal Network (CNN) for image classification deployed on distributed infrastructures.
翻译:企业系统目前的发展动态观察了范式的转变,将针头从后端移到边缘部分;通过分发数据、分散应用和将新组成部分无缝地整合到中央系统;分散部署的AI能力将推动这一转变。随着这些发展动态,出现了若干不起作用的要求,而安全是讨论的中心。铭记这些要求,我们在此提出一种全面保护分布式深神经网络基于/强化的分布式深神经网络软件资产的方法,即其输入和输出数据流的保密性以及保护其知识产权。我们的方法是利用全同质加密(FHE)来保护分布式神经网络,同时处理加密数据。在这方面,我们评估关于分布式基础设施图像分类的革命性神经网络(CNN)解决方案的可行性。