With the advances in 5G and IoT devices, the industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding 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 IIoT environment. We proposed a three-layer encryption method for data security and provide 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.
翻译:随着5G和IoT设备的进步,这些行业正在广泛采用人工智能技术来改进分类和预测服务,然而,使用AI也引起了对隐私和安全的关切,这些隐私和安全可能被滥用或泄漏。私人AI是最近通过将AI与加密技术相结合而为处理数据安全问题而发明的,但现有的研究表明,可使用模型反向攻击来从模型参数中使图像发生逆转。在这方面,我们提议采用基于Freeded Learning和加密的私人(FLEP)AI框架,为IIOT环境中的数据和模型参数提供两级安全。我们提出了数据安全的三层加密方法,并为模型参数的安全提供了假设方法。实验结果显示,拟议的方法以略微增加的执行时间为代价,提高了加密质量。我们还强调了与FLEPAI框架实现有关的若干公开问题和挑战。