Privacy-Preserving Uncertainty Calibration Using Perceptual Encryption in Cloud–Edge Collaborative Artificial Intelligence of Things

Cloud-edge collaboration provides an efficient way to promote the development and application of the Artificial Intelligence of Things (AIoT) by addressing the limited computing, communication, and storage capabilities of the Internet of Things (IoT) devices. The compute-intensive task of developing an Artificial Intelligence (AI) model is performed on the cloud-server over a public dataset while it is deployed on the edge-server to analyze the IoT data. The merit in processing data locally is the privacy preservation. However, to keep the model relevant to the ever-growing IoT data, it is necessary to share it with the cloud-server, which can raise certain privacy concerns. Also, it is challenging to adapt the model to the IoT data characteristics. Therefore, we propose a privacy-preserving cloud-edge paradigm incorporating personalized context learning and incremental learning strategies to adapt the trained model to the heterogeneous and dynamic IoT environment. To avoid any privacy issues, we propose a lightweight image cryptosystem parameterized to protect image contents according to an application requirement. The simulation results show that our proposed image cryptosystem has favorable encryption properties and the cipher-images are learnable by an AI model. In addition, our incremental learning strategy efficiently kept the model up-to-date, and with proposed personalized context learning strategy, the cloud-server model’s performance improved up to 13% for plain-images and 18% for cipher-images.