Privacy Protection in Mobile Big Data: Challenges and Solutions

Abstract

The pervasive use of mobile big data has profoundly altered daily life, providing unprecedented convenience and efficiency. However, with the proliferation of mobile devices and the explosive growth of data volume, the issue of user privacy protection has become increasingly severe. Location information and semantic information, as the two core components of mobile big data, can directly reflect users’ behavioral trajectories and thought dynamics, underscoring the importance of privacy protection. Although existing technologies can protect user data to a certain extent, traditional methods struggle to address increasingly sophisticated attack techniques in the face of evolving privacy threats. A comprehensive privacy protection scheme for mobile big data was proposed in this study, with a focus on two main areas: the privacy protection of location-based and semantic-based mobile big data. For location information protection, an uncertain graph model was employed to effectively resist combined attacks by jointly protecting the user layer and the location layer. For semantic information protection, a hypergraph clustering method was used to structurally protect the user layer and the semantic layer, enhancing the privacy security of semantic information. This study not only addresses existing gaps but also provides new solutions for mobile big data privacy protection, offering significant theoretical and practical value.