Dynamic Privacy Protection with Large Language Model in Social Networks

Yizhe Xie, Congcong Zhu, Xinyue Zhang, Xiangyu Hu, Xuan Liu

Abstract: In contemporary social networks, dynamic privacy protection remains a pivotal yet challenging endeavor due to the intricate and evolving nature of information exchange. Traditional privacy models, predominantly static, falter in effectively safeguarding privacy amidst the complex interplay of continuously changing network interactions and structures. Addressing these deficiencies, this study introduces a novel dynamic privacy protection system anchored by large language model (LLM). Leveraging the natural language processing prowess of LLM, this system excels in real-time, context-sensitive analysis and protection of textual data within vast and variable social networks. By integrating closed-loop control theory, the system adeptly balances robust privacy safeguards with the requisite fluidity of information exchange. Experimental validations on large network datasets illustrate the system’s adeptness in balancing privacy leaks and information distortion through intelligent adaptations to privacy thresholds and strategic noise injection. The outcomes highlight the system’s utility in enhancing data security and operational efficiency, promising significant implications for future applications in broader domains such as mobile computing and IoT. This study not only propels forward the capabilities of dynamic privacy protection mechanisms but also sets a foundational architecture for subsequent innovations in privacy-sensitive, data-intensive environments.

External IDs:dblp:conf/ica3pp/XieZZHL24