Facial recognition and resolution technology have extensive application scenarios in the era of big data. It ensures the consistency of personal identity in physical space and cyberspace by establishing correspondence between physical objects and network entities. However, massive data brings huge processing pressure to cloud service, and there are data leakage risks about personal information. To address this problem, we propose a privacy security protection scheme for facial recognition and resolution based on edge computing. Firstly, a facial recognition and resolution framework based on edge computing is established, which improves the communication and storage efficiency through task partition and relieves the pressure of cloud computing. Then, a verifiable deletion scheme based on Hidden CP-ABE is proposed to provide fine-grained access control and ensure the safe deletion of target data in the cloud. Moreover, after applying the verifiable deletion method, the safe deletion of the target data in the cloud can be achieved. Finally, the simulation results show the effectiveness and security of the proposed scheme.

The application of digital tracking mechanism introduces a series of leakage problems of users' personal sensitive information related to the trajectory. Therefore, we propose a secure storage scheme for trajectory data. Firstly, four‐dimensional spatiotemporal clustering of the trajectory data is performed to reduce the spatiotemporal complexity of data storage. Secondly, the privacy level of the trajectory data in the clusters is measured individually, which ensures the needs for personalized privacy protection are met. Finally, a noise trajectory (NTR) tree based on differential privacy is constructed, and the allocation of privacy budget and noise addition are optimized. Extensive simulations show that our scheme improves in terms of time efficiency, and achieves a flexible and effective balance between data accuracy and privacy.