题目：Security and Privacy for Mobile Social Networks 时间：2016年5月5日(周四)下午3:00-3:50 地点：玉泉校区行政楼108学术会议室 报告人：Kuan Zhang, University of Waterloo

专家介绍： Kuan Zhang received the B.Sc. degree in Communications Engineering and M.Sc. degree in Computer Science from Northeastern University, Shenyang, China, in 2009 and 2011, respectively. He received the Ph.D. degree in Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, in 2016. His current research interests include security and privacy for mobile social networks, e-healthcare, Internet of Things, and cloud computing. Dr. Zhang served as the Technical Program Committee Member for IEEE ICC’14, Globecom'15, ICNC'16, VTC-Spring'16, VTC-Fall'16 and CHASE’16. He is a co-recipient of 2013 IEEE WCNC Best Paper Award.

报告内容：With the ever-increasing demands of people's social interactions, traditional online social networking applications are being shifted to the mobile ones, enabling users' social networking and interactions anywhere anytime. Due to the portability and pervasiveness of mobile devices, such as smartphones, wearable devices and tablets, Mobile Social Network (MSN), as a prestigious social network platform, has become increasingly popular and brought immense benefits. However, the flourish of MSNs also hinges upon fully understanding and managing the challenges, such as security threats and privacy leakage. Security and privacy concerns rise as the boom of MSN applications comes up, and few users have paid adequate attentions to protect their privacy-sensitive information from disclosing. In this seminar, we present security and privacy challenges in MSNs, and focus on adjustable and user-centric protections. We also introduce several challenging issues, including spam, misbehaviors and privacy leakage. To tackle these problems, we propose efficient security and privacy preservation schemes for MSNs. Firstly, to address the issues of spam in autonomous MSNs, we propose a personalized fine-grained spam filtering scheme (PIF), which exploits social characteristics during message delivery to block spam in a privacy-preserving way. Secondly, to detect misbehaviors during MSN data sharing, we propose a social-based mobile Sybil detection scheme (SMSD). The SMSD detects Sybil attackers by differentiating the abnormal pseudonym changing and contact behaviors, since Sybil attackers usually frequently or rapidly change their pseudonyms to cheat legitimate users.