# Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection Source: [Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:40:30.056Z Authors: Laurent Gomez, Marcus Wilhelm, Jos\\'e M\\'arquez, Patrick Duverger Published: 2019-07-09T15:29:42.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()