An Efficient Image Privacy Preservation Scheme for Smart City Applications Using Compressive Sensing and Multi-Level Encryption
Xiaofei He, Lixiang Li, Haipeng Peng, Fenghua Tong
Abstract
With the rapid advancement of smart cities, the utilization of digital images has become widespread, particularly in services such as urban traffic management and public space security surveillance. However, the acquisition, transmission and sharing of digital images inevitably raise concerns about privacy disclosure. To address this challenge, we propose a lightweight image encryption scheme based on data hiding and compressive sensing (CS). Specifically, during the CS sampling and compression stages, we employ data-hiding techniques to embed information from the confusion matrix and coordinates of sensitive regions into CS ciphertext, ensuring the secure transmission of encryption keys for sensitive regions. Additionally, our solution can provide personalized access control mechanisms based on the permission levels of different authorized users and offer customized image recovery quality according to their specific requirements. This effectively addresses the potential privacy leakage risks associated with cross-departmental image sharing, ensuring the security of data transmission and sharing processes. Under consistent experimental conditions, our proposed solution demonstrates a minimum 1.5% improvement in reconstruction quality compared to existing methods. In the security analysis, we further demonstrate that the proposed scheme provides differential reconstruction quality and high-security strength for staff members with different permissions. We believe the proposed solution can suit many practical applications in smart cities.