Open Access

ARTICLE

Practical Privacy-Preserving ROI Encryption System for Surveillance Videos Supporting Selective Decryption

Chan Hyeong Cho, Hyun Min Song^*, Taek-Young Youn^*

Department of Cyber Security, Dankook University, Yongin, Gyeonggi-Do, 16890, Republic of Korea

* Corresponding Authors: Hyun Min Song. Email: ; Taek-Young Youn. Email:

(This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)

Computer Modeling in Engineering & Sciences 2024, 141(3), 1911-1931. https://doi.org/10.32604/cmes.2024.053430

Received 30 April 2024; Accepted 20 August 2024; Issue published 31 October 2024

Abstract

With the advancement of video recording devices and network infrastructure, we use surveillance cameras to protect our valuable assets. This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security. The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest (ROIs) within the video, linking these ROIs to generate unique IDs. These IDs are then combined with a master key to create entity-specific keys, which are used to encrypt the ROIs within the video. This system supports selective decryption, effectively protecting personal information using surveillance footage. Additionally, the system overcomes the limitations of existing ROI recognition technologies by predicting unrecognized frames through post-processing. This research validates the proposed technology through experimental evaluations of execution time and post-processing techniques, ensuring comprehensive personal information protection. Guidelines for setting the thresholds used in this process are also provided. Implementing the proposed method could serve as an effective solution to security vulnerabilities that traditional approaches fail to address.

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Keywords

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