VidSearch: Privacy-by-Design Video Search and Retrieval System for Large-Scale CCTV Data

Tahir, Mehwish; Qiao, Yuansong; Kanwal, Nadia; Lee, Brian; Asghar, Mamoona Naveed

Abstract

The surge in surveillance camera deployment in the era of Big Data and the Internet of Things (IoT) has emphasized the paramount importance of safeguarding the privacy of individuals, objects, and locations they record. Therefore, this paper proposes VidSearch – a secure system designed for storing, searching, and retrieving videos captured by CCTV cameras. VidSearch system enhances visual data protection through encryption, query-by-text video searching within encrypted data, and anonymized video retrieval using pixelization. During storage, encrypted videos and their metadata are stored separately to facilitate text-based search and video retrieval from encrypted videos. Fernet encryption is applied to protect videos, and two anonymization algorithms i.e., a Mixture of Gaussians 2 (MOG2) and K-Nearest Neighbors (KNN) are used for detecting the foreground (moving objects) and background of the videos at the retrieval stage. Video retrieval results demonstrate that KNN excels in accuracy for visual content detection, while MOG2 is more efficient in terms of processing time. VidSearch system is extensively tested on a general-purpose Intel system and an IoT NVIDIA Jetson. Results confirm the system's ability to operate in a Big Data and IoT ecosystem across multiple devices and platforms.

Citation

Tahir, M., Qiao, Y., Kanwal, N., Lee, B., & Asghar, M. N. (2023, December). VidSearch: Privacy-by-Design Video Search and Retrieval System for Large-Scale CCTV Data. Presented at 2023 International Conference on Machine Learning and Applications (ICMLA), Jacksonville, FL, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 International Conference on Machine Learning and Applications (ICMLA)
• Start Date: Dec 15, 2023
• End Date: Dec 17, 2023
• Acceptance Date: Dec 15, 2023
• Publication Date: Dec 15, 2023
• Deposit Date: Apr 12, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• ISBN: 979-8-3503-1891-3
• DOI: https://doi.org/10.1109/icmla58977.2023.00329
• Public URL: https://keele-repository.worktribe.com/output/790692
• Publisher URL: https://ieeexplore.ieee.org/document/10460003
• Related Public URLs: https://ieeexplore.ieee.org/xpl/conhome/10459339/proceeding