With the advances in 5G and IoT devices, industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding data privacy and security that can be misused or leaked. Private AI was recently coined to address the data security issue by combining AI with encryption techniques, but existing studies have shown that model inversion attacks can be used to reverse engineer the images from model parameters. In this regard, we propose a federated learning and encryption-based private (FLEP) AI framework that provides two-tier security for data and model parameters in an Industrial IoT environment. We propose a three-layer encryption method for data security and provided a hypothetical method to secure the model parameters. Experimental results show that the proposed method achieves better encryption quality at the expense of slightly increased execution time. We also highlight several open issues and challenges regarding the FLEP AI framework's realization.

中文翻译：

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迈向工业私有人工智能：数据和模型安全的两层框架

随着 5G 和物联网设备的进步，各行业正在广泛采用人工智能 (AI) 技术来改进基于分类和预测的服务。然而，人工智能的使用也引发了对可能被滥用或泄露的数据隐私和安全性的担忧。最近创造了私有 AI，通过将 AI 与加密技术相结合来解决数据安全问题，但现有研究表明，模型反转攻击可用于从模型参数对图像进行逆向工程。在这方面，我们提出了一个联邦学习和基于加密的私有 (FLEP) AI 框架，该框架为工业物联网环境中的数据和模型参数提供两层安全性。我们提出了一种用于数据安全的三层加密方法，并提供了一种保护模型参数的假设方法。实验结果表明，所提出的方法以略微增加执行时间为代价实现了更好的加密质量。我们还强调了有关 FLEP AI 框架实现的几个未解决的问题和挑战。