In the context of big data, trajectory data, as an important resource in areas such as location services, urban planning, and business analysis, continues to expand its collection and application scope. However, sensitive information such as user behavior characteristics and social relationships contained in it can easily be uniquely identified through a few location points, leading to privacy leakage risks. Existing protection technologies (such as encryption and anonymization) have problems of being cracked or data distortion, while the integration of differential privacy, 5G edge computing and artificial intelligence provides a new path to balance privacy and availability. This research focuses on the privacy protection of trajectory data throughout its life cycle, and proposes two innovative mechanisms: first, the trajectory differential privacy protection mechanism (5GMEC-DP) based on 5G mobile edge computing improves the Geo Indistinguishability method through the client coordinate differential privacy algorithm and the MEC server truncation mechanism. When the privacy budget ε=0.001 and ε=0.0005, the average availability (AQ) of trajectory coordinates decreases by 64% and 81% respectively compared with the traditional methods, and the overall database availability loss (HD) decreases by 64% and 82%; Secondly, based on the LSTM-GAN trajectory data differential privacy publishing mechanism (LGAN-DP), combined with deep learning models to generate synthetic trajectories and apply differential privacy processing result sets, its HD index is improved by 40% to 50% compared to existing methods, and its time complexity is lower. In addition, a trajectory privacy protection and publishing system has been developed that integrates modules such as visualization, encryption, and caching. Currently, the system focuses on location encryption and trajectory publishing, and plans to expand modules such as peripheral recommendation and travel prediction in the future. Through the integration of 5G edge computing and deep learning model, this research provides an efficient and scalable technical solution for track data privacy protection, which significantly improves the balance between privacy and availability compared with traditional methods.

Yantao Tao. Research on Computer Network Information Security and Protection under the Background of Big Data. International Journal of Neural Network (2025), Vol. 4, Issue 1: 1-11. https://doi.org/10.38007/NN.2025.040101.

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