IoT CCTV Video Security Optimization Using Selective Encryption and Compression

Data security and privacy are critical concerns when integrating Closed-Circuit Television (CCTV) cameras with the Internet of Things (IoT). To enhance security, IoT data must be encrypted before transmission and storage. However, to minimize overheads related to storage space, computational time, and transmission energy, data can be compressed prior to encryption. H.264/AVC (Advanced Video Coding) offers a balanced solution for video compression by addressing processing demands, video quality, and compression efficiency. Encryption is vital for safeguarding data security, yet the integrity of IoT data may sometimes be compromised. Ineffective data selection can lead to inefficiencies and potential security risks, highlighting the importance of addressing CCTV video data security carefully. This study proposes an algorithm that integrates compression with selective encryption techniques to reduce computational overhead while ensuring access to critical information for real-time analysis. By employing frame intervals, the algorithm enhances efficiency without compromising security. The execution details and merits of the proposed approach are analyzed, demonstrating its effectiveness in safeguarding the privacy and integrity of IoT CCTV video data. Results reveal superior performance in terms of compression efficiency and encryption/decryption times, with an average encryption time of 0.00171 seconds for a 128-bit key, enabling fast processing suitable for real-time applications. The decryption time matches the encryption time, confirming the method’s viability for practical IoT CCTV implementations. Metrics such as correlation coefficient, bitrate overhead, and histogram analysis further validate the approach’s robustness against statistical attacks.

@article{Kaur2025,
  title     = {IoT CCTV Video Security Optimization Using Selective Encryption and Compression},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Kawalpreet Kaur and Amanpreet Kaur and Yonis Gulzar and Vidhyotma Gandhi and Mohammad Shuaib Mir and Arjumand Bano Soomro},
  doi       = {10.14569/IJACSA.2025.0160228},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160228}
}