Motion vector encryption in multimedia streaming

Streaming video application requires high security as well as high computational performance. In video encryption, traditional selective algorithms have been used to partially encrypt the relatively important data in order to satisfy the streaming performance requirement. Most video selective encryption algorithms are inherited from still image encryption algorithms, the encryption on motion vector data is not considered. The assumption is that motion vector data are not as important as pixel image data. Unfortunately, in some cases, motion vector itself may be sufficient enough to leak out useful video information. Normally motion vector data consume over half of the whole video stream bandwidth, neglecting their security may be unwise. In this paper, we target this security problem and illustrate attacks at two different levels that can restore useful video information using motion vectors only. Further, an information analysis is made and a motion vector information model is built. Based on this model, we describe a new motion vector encryption algorithm called MVEA. We show the experimental results of MVEA. The security strength and performance of the algorithm are also evaluated.

A software engineering framework for context-aware pervasive computing

There is growing interest in the use of context-awareness as a technique for developing pervasive computing applications that are flexible, adaptable, and capable of acting autonomously on behalf of users. However, context-awareness introduces various software engineering challenges, as well as privacy and usability concerns. In this paper, we present a conceptual framework and software infrastructure that together address known software engineering challenges, and enable further practical exploration of social and usability issues by facilitating the prototyping and fine-tuning of context-aware applications.