Enforcing Privacy Preservation on Edge Cameras Using Lightweight Video Frame Scrambling

Alem Fitwi, Yu Chen, Sencun Zhu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Protecting privacy is a very challenging task in a highly surveilled world with zillions of surveillance cameras deployed ubiquitously. The difficulty mainly lies in the facts: (i) there is not a distinctively defined boundary between usability and privacy, (ii) video frames indiscriminately created and collected by the edge cameras could be abused and intercepted, and (iii) it is difficult to enforce the commonly used compute-intensive standard techniques as-is on the edge cameras because of limited computational resources. In this paper, we propose a lightweight and secure scheme to Enforce Privacy-preservation on Edge Cameras (EnPEC) using deep learning and a sinusoidal chaotic-map. The proposed EnPEC architecture comprises a lightweight frame classifier designed to label frames as offensive and harmless depending on their content to ensure the practice of selective surveillance following a frame approximation process and a novel sinusoidal-map-based chaotic image scrambling technique that enciphers frames color-channel wise to ensure end-to-end (E2E) privacy of frame contents. The extensive analysis of the functionality, performance and security of the EnPEC scheme, and comparison with related works verify that the EnPEC scheme is more feasible, robust and secure when it runs in real-time on edge cameras equipped with a computational power equivalent to the Raspberry PI 4.

Original languageEnglish (US)
Pages (from-to)276-287
Number of pages12
JournalIEEE Transactions on Services Computing
Volume16
Issue number1
DOIs
StatePublished - Jan 1 2023

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management

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