Health Data Sharing with Misbehavior Detection

Abstract

Despite the traditional health monitoring, MHNs can offer a wide range of social network and information sharing applications. In these social applications, MHNs are vulnerable to the malicious attacks and misbehaviors of mobile users, which may degrade the performance and even disrupt MHNs. The attackers may forge their social attributes to snatch other legitimate users’ health information during the information sharing, which violates users’ privacy. It may help them to push some biased health product recommendations and spam [1]. Moreover, these attackers may also misbehave, e.g., not following the network protocol, spreading spams, launching Denial-of-Service (DoS) attacks or consuming a large amount of network resources. Although some misbehavior detection schemes [2] can partially resist certain types of attacks, it is still challenging to adjust the security protection against the powerful attacks, such as Sybil attacks. The cost of misbehavior detection may increase due to the skyrocketing attacking capabilities of these attackers. To offer MHNs from QoP perspective, the misbehaviors should be categorized into different levels with the corresponding detection or protection schemes. In this chapter, we present social based mobile Sybil detection scheme to differentiate malicious attackers from normal users.