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.

大型视频监控系统越来越被视为解决公共安全、执法和公共场所态势感知等问题的解决方案。然而，未经授权使用源自视频数据的个人信息可能是有害的。为了保护隐私，重要的是要了解视频监控数据中包含哪些类型的个人信息，以及该信息中有多少对于观察者实现其授权目的至关重要。观察者的目的是用类似于视频监控系统提取的信息的术语来描述的，因此它们可以进行比较。这有助于确定哪种类型的信息更适合控制信息流向多个观察者，而不会损害个人的隐私。