Metadata based need-to-know view in large-scale video surveillance systems

Abstract

Large-scale video surveillance systems are increasingly seen as the answer to problems concerning public safety, law enforcement, and situational awareness in public places. However, the unauthorized use of personal information derived from video data can be harmful. To preserve privacy, it is important to understand what type of personal information is contained in video surveillance data, and how much of that information is essential for an observer to achieve her authorized purpose. The purpose of the observer is described in terms similar to the information extracted by video surveillance systems, so they can be compared. This helps identify what type of information is better suited to control the flow of information to multiple observers, without compromising the privacy of the individuals. This paper presents a privacy-aware and need-to-know access control framework built on fine-grained data properties, extracted from surveillance data, which must conform to the explicitly defined purpose of the observers.