Innovation of Privacy Protection Mode of Mobile Social Networks in Big Data Era

With the deep integration of mobile Internet and big data technology, mobile Social networks (MSNs) have become an indispensable digital living space in modern society. However, the real-time collection, analysis and sharing of massive user data leads to a systematic and complex trend of privacy leakage risks. This paper analyzes new challenges such as predictive deanonymization attacks (PDAA), lack of context awareness, and quantum computing threats, and proposes an innovative multilayer dynamic privacy protection architecture (MDPPA). By integrating key technologies such as differential privacy optimization mechanism, federated learning system, blockchain technology and artificial intelligence monitoring, the architecture constructs a context-aware, user-controllable and resilient privacy protection system. Experimental results show that the proposed model improves accuracy by 14% and communication cost by 90% compared with traditional federated learning in extreme non-independent and identically distributed data scenarios, while providing verifiable privacy protection. The research further discusses the future development direction of anti-quantum encryption and cross-chain interoperability, and provides theoretical support and technical path for building a "user-centered" next-generation privacy protection paradigm.