Int. J. Trust Management in Computing and Communications, Vol. 1, No. 1, 2013 23 Copyright © 2013 Inderscience Enterprises Ltd. Privacy aware publication of surveillance video Mukesh K. Saini* National University of Singapore, AS6, #05-19, 13 Computing Drive, 117417, Singapore E-mail: mksaini@comp.nus.edu.sg *Corresponding author Pradeep K. Atrey University of Winnipeg, 515 Portage Avenue, Winnipeg, MB R3B2E9, Canada E-mail: p.atrey@uwinnipeg.ca Sharad Mehrotra Department of Computer Science, University of California, Irvine, CA, 92601, USA E-mail: sharad@ics.uci.edu Mohan S. Kankanhalli National University of Singapore, AS6, #05-06, 13 Computing Drive, 117417, Singapore E-mail: mohan@comp.nus.edu.sg Abstract: Current surveillance systems record an enormous amount of video footage everyday. This video contains events and activities of real life which are useful in many applications. In this paper, we explore privacy preserving publication surveillance video footage, which requires robust privacy modelling and selection of appropriate data transformation function. Traditional privacy protection methods only consider implicit channels of privacy loss (such as facial information), ignoring other implicit channels. The proposed privacy model consolidates the identity leakage through both implicit and explicit channels. To choose data transformation function, we propose computational models for privacy loss and utility loss and study the tradeoff between these two. Experiments show that the hybrid data transformation method (using a combination of quantisation and blurring) provides the best tradeoff between privacy and utility. Furthermore, applying blurring first and then quantising gives better results. Keywords: surveillance video; privacy; data publication.