W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video

  • Authors:

  • Mukesh Saini;Pradeep K. Atrey;Sharad Mehrotra;Mohan Kankanhalli

  • Affiliations:

  • School of Computing, National University of Singapore, Singapore, Singapore;Department of Applied Computer Science, The University of Winnipeg, Winnipeg, Canada;Information and Computer Science Department, University of California, Irvine, USA;School of Computing, National University of Singapore, Singapore, Singapore

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2014

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Abstract

Huge amounts of video are being recorded every day by surveillance systems. Since video is capable of recording and preserving an enormous amount of information which can be used in many applications, it is worth examining the degree of privacy loss that might occur due to public access to the recorded video. A fundamental requirement of privacy solutions is an understanding and analysis of the inference channels than can lead to a breach of privacy. Though inference channels and privacy risks are well studied in traditional data sharing applications (e.g., hospitals sharing patient records for data analysis), privacy assessments of video data have been limited to the direct identifiers such as people's faces in the video. Other important inference channels such as location (Where), time (When), and activities (What) are generally overlooked. In this paper we propose a privacy loss model that highlights and incorporates identity leakage through multiple inference channels that exist in a video due to what, when, and where information. We model the identity leakage and the sensitive information separately and combine them to calculate the privacy loss. The proposed identity leakage model is able to consolidate the identity leakage through multiple events and multiple cameras. The experimental results are provided to demonstrate the proposed privacy analysis framework.