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The crowdsourcing-based parking reservation system is a new computing paradigm, where private owners can rent their parking spots out. Security is the main concern for parking reservation systems. However, current schemes cannot provide user privacy protection for drivers and have no key agreement functions, resulting in a lot of security problems. Moreover, current schemes are typically based on the time-consuming bilinear pairing and not suitable for real-time applications. To solve these security and efficiency problems, we present a novel security protocol with user privacy called SCPR. Similar to protocols of this field, SCPR can authenticate drivers involved in the parking reservation system. However, different from other well-known approaches, SCPR uses pseudonyms instead of real identities for providing user privacy protection for drivers and designs a novel pseudonym-based key agreement protocol. Finally, to reduce the time cost, SCPR designs several novel cryptographic algorithms based on the algebraic signature technique. By doing so, SCPR can satisfy a number of security requirements and enjoy high efficiency. Experimental results show SCPR is feasible for real world applications.

This chapter aims to explore the use of precautionary behaviors by public transit users. It distinguishes between two types of precautionary behavior: avoidance and risk management strategies. Following a literature review on precaution nary behaviors, the chapter draws data from five of the cities examined earlier in this book -Guangzhou (China), London (UK), Los Angeles (USA), Paris (France), and Vancouver (Canada) to examine how student riders in these cities respond to the risk of victimization in transit environments. This is followed by a discussion of the findings, conclusions, and an overall assessment of the findings.

Over the past ten years face segmentation has developed rapidly and various algorithms have been proposed. In this paper we will demonstrate a face detection system based on skin color and the spaces RGB, normalized RGB, HSV and YCbCr are concentrated here. Through combing them the more accurate face region will be detected.