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 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 IIoT environment. We proposed 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 highlighted 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,AI的使用也引起了对数据隐私和安全的关切,这些数据隐私和安全可能被滥用或泄漏。私人AI最近通过将AI与加密技术相结合而创建,以解决数据安全问题,但现有研究表明,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,我们提议了一个联合学习和加密私营(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三层加密方法,并提供了一种假设方法,确保模型参数的安全。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框架实现情况有关的若干公开问题和挑战。