With the advance of cloud computing, growing applications have been migrating to the cloud for its robustness and scalability. However, sending raw data to the cloud-based service providers will generally risk our privacy; especially for cloud-based surveillance system, where privacy is one of the major concerns as continuously recording daily life. Thus, privacy-preserving intelligent analytics are in dire needs. In this preliminary research, we investigate real-time privacy-preserving moving object detection in the encrypted cloud-based surveillance videos. Moving object detection is one of the core techniques and can further enable other applications (e.g., object tracking, action recognition, etc.). One possible approach is using homomorphic encryption which provides corresponding operations between unencrypted and encrypted data. However, homomorphic encryption is impractical in real case because of formidable computations and bulky storage consumption. In this paper, we propose an efficient and secure encryption framework, which entails real-time analytics (e.g., moving object detection) in encrypted video streams. Experiments confirm that the proposed method can achieve similar accuracy as detection on original raw frames.

One of the first view privacy-preserving video object detection.