Privacy aware surveillance system design

Abstract

Video surveillance is a very effective means of monitoring activities over a large area with cameras as extended eyes. However, this additional security comes at the cost of privacy loss of the citizens not involved in any illicit activities. The traditional privacy protection methods only consider facial cues for identity leakage and privacy loss. Because an adversary can use prior knowledge to infer the identities even in the absence of the facial information, we propose a privacy-aware surveillance framework in which we identify the implicit channels that cause identity leakage, quantify privacy loss through non-facial information, and propose solutions to block these channels for near zero privacy loss with minimal utility loss. Privacy loss is modeled as an adversary's ability to correlate sensitive information to the identity of the individuals in the video. Anonymity based approach is used to consolidate the identity leakage through explicit channels of bodily cues such as facial information; and other implicit channels that exist due to 'what', 'when' and 'where' information. The proposed privacy model is applied to two important applications of surveillance video data publication and CCTV monitoring. Through experiments it is found that current privacy protection methods include high risk of privacy loss while the proposed framework provides more robust privacy loss measures and better tradeoff of security and privacy.