Moving Object Detection (MOD) can be outsourced to the cloud for computational convenience, in which case the video must be encrypted to protect privacy. Secure video MOD methods designed to perform MOD on encrypted video are still in their infancy. In this paper, we present an attention-based framework for privacy-preserving MOD in compressed videos. On the user side, we adopt selective video encryption for the compressed video, while in the cloud, we extract the Compressed Video entropy-coded Syntax Elements (CVSE) from the encrypted video. Since the extracted CVSE data lacks sufficient motion information and contains noise, we introduce a two-stage training process. In the first phase, we propose a new deep learning-based approach for interpolating CVSE-based motion feature maps, addressing a significant drawback of traditional methods that rely exclusively on empirical interpolation algorithms. In the second stage, we propose a specialized backbone tailored for feature extraction from sparse CVSE data. We then design an attention-based neck that focuses on areas with denser motion and varying sizes of moving objects. Experimental results on two public datasets, VIRAT and DUKE-MTMC, show that our framework achieves state-of-the-art detection performance. Compared to previous secure solutions, the proposed method exhibits more robustness in challenging scenes.

In the cloud, there is an urgent need to implement intelligent video surveillance in a privacy-preserving way. Moving object detection is an important task in the intelligent surveillance system. In this paper, we propose a privacy-preserving deep learning framework to detect moving objects on compressed videos. We encrypt video bitstreams using selective video encryption to protect the private video content. We propose encrypted domain motion information (EDMI) without decryption and decompression to design three motion feature maps. Due to the sparsity of the EDMI distribution, existing convolutional backbones designed for RGB images have difficulty providing satisfactory performance. We design a novel convolutional backbone using a ”subtraction” strategy to reduce model complexity. Our backbone employs residual blocks and skipping connections to reuse the EDMI at deeper layers. We evaluate our model on two large high-definition surveillance video datasets, i.e., VIRAT and Duke-MTMC. The experimental results show that the proposed framework achieves state-of-the-art detection performance compared with the most recent works. Our approach achieves an excellent privacy-utility tradeoff. Compared to previous solutions, it performs more robustly in crowded scenarios with challenges like occlusion. To our best knowledge, this is the first reported deep learning framework for moving object detection in encrypted-compressed video.

Frequent itemset mining is a data mining technique widely used on massive datasets. In cloud computing, the dataset may be encrypted for privacy protection. Therefore, frequent itemset mining over encrypted data is a crucial application in secure cloud computing. In this paper, we propose an effective privacy-preserving framework where the cloud server can directly perform data mining on the encrypted database without interacting with other cloud servers. We first design three security primitives to implement subset determination, accumulation, and comparison in the encrypted domain for frequent itemset mining. Based on the proposed framework, we then propose two secure protocols that allow the cloud server to perform frequent itemset mining on encrypted cloud data with these security primitives. The first protocol leaks no information to the cloud and the second protocol has the advantage of more efficient mining performance. We then present two strategies with parallel algorithms and GPU computing to accelerate the running time. We also analyze the security of our protocols and the computational complexities. Experimental results show that our serial-based protocols achieve shorter running times and higher levels of privacy than previous solutions. Our multi-CPU (or GPU) based parallel protocol can further reduce the practical running time.

Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of 4×4 pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows.