Mobile Social Network (MSN), as an emerging social network platform, has become increasingly popular and brought immense benefits. However, big data challenges and security concerns rise as the boom of MSN applications comes up. In this talk, we will present big data and security challenges in MSNs, and introduce big data analysis solutions. First, to detect misbehaviors during data sharing, we present a social-based mobile Sybil detection scheme (SMSD). The SMSD analyzes user's social behaviors during networking and 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. Then, we introduce a social network based infection analysis system, to analyze the instantaneous infectivity during human-to-human contact. We also present privacy-preserving data query and classification methods to achieve big data analysis and privacy in this infection analysis system. This talk will close with a brief discussion of future work on big data and security.