# Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection

## TL;DR

This paper proposes a holistic security approach for distributed deep neural networks using Fully Homomorphic Encryption to protect data confidentiality and intellectual property in distributed AI systems.

## Contribution

It introduces a novel method employing Fully Homomorphic Encryption to secure distributed DNNs, addressing confidentiality and IP protection in decentralized AI deployments.

## Key findings

- Feasibility demonstrated on CNN for image classification
- Secure processing of encrypted data in distributed neural networks
- Enhanced protection of data streams and intellectual property

## 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.