Secure and Efficient Video Surveillance in Cloud Computing

Video Surveillance has been widely used in business establishments. Since digital cameras everlastingly collect the video data, the volume of sampled data is extensively large, which is hard to be stored and managed locally. Outsourcing surveillance video data to the cloud can achieve cost saving and flexibility, but also will incur potential privacy leakage. In this paper, we utilize the Compressed Sensing (CS) technique for sampling and compressing, to achieve secure and efficient video surveillance in cloud computing. Firstly, we identify the known-plaintext attack in such environment. That is, given sufficient information about the original signal and corresponding CS measurements, the attacker is likely to calculate the measurement matrix. Then, we propose a Dynamic Compressive Sensing(DCS) scheme to resist such an attack. Specifically, we use a dynamic measurement matrix that is changeable over time to prevent the attackers from gaining sufficient information to calculate the measurement matrix. Furthermore, we allow the cloud to help users decode the non-reference frames without leaking any information, to take full advantage of the powerful computing. Experimental results show that the proposed scheme effectively protects the security of the surveillance video, and provides a good recovery quality for users to conduct further analysis.