This research focuses on the balance between privacy protection and data practicality of high-dimensional location data in the mobile Internet and LBS scenarios. Traditional anonymization and perturbation methods suffer from precision loss, weak dynamic adaptability, and neglect of personalized user needs. Based on the rigor of differential privacy theory, two innovative solutions are proposed: firstly, the graph automatic encoding method integrates user social relationships and spatial behavior, and achieves joint protection of trajectory and topology through dynamic privacy budget allocation; Secondly, building personalized privacy configurations based on user preferences, allocating privacy budgets differentially through stopping point clustering and sensitivity scoring, and enhancing service accuracy while strengthening protection in sensitive areas. Experimental verification shows that both perform excellently in terms of privacy protection strength, data utility (improved by over 10%), and operational efficiency. Dynamic budget allocation and user preference modeling are key to balancing privacy and practicality. In the future, intelligent parameter adjustment, large-scale scene adaptation, and compliance verification will be explored.

Yiting Hong. Differentially Private High-Dimensional Business Data Publishing and Analysis Algorithm. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 28-35. https://doi.org/10.38007/IJBMET.2026.070104.

[1] Ni G, Sun J. Differential privacy protection algorithm for large data sources based on normalized information entropy Bayesian network. 2024.

[2] Sheng J, Zhang L, Zhang Y, et al. A Localization Correction Algorithm of Location-Based Services Based on Point Clustering[C]//International Conference on Data Mining and Big Data. Springer, Singapore, 2024.

[3] Li L, Flynn T, Hoisie A. Learning Independent Program and Architecture Representations for Generalizable Performance Modeling. 2023.

[4] Sanwal S. Design and Architecture for a Centralized, Extensible, and Configurable Scoring Application. 2023.

[5] Huang, J. (2025). Research on Cloud Computing Resource Scheduling Strategy Based on Big Data and Machine Learning. European Journal of Business, Economics & Management, 1(3), 104-110.

[6] Lu, Z. (2025). AI-Driven Cross-Cloud Operations Language Standardisation and Knowledge Sharing System. European Journal of AI, Computing & Informatics, 1(4), 43-50.

[7] Alikhanifard P, Tsantalis N. A Novel Refactoring and Semantic Aware Abstract Syntax Tree Differencing Tool and a Benchmark for Evaluating the Accuracy of Diff Tools. ACM Transactions on Software Engineering and Methodology, 2025, 34(2).

[8] Zhang Z, Song Y. Spatial Big Data and Analysis Strategies Supporting Geographic Information System for Transportation (GIS-T) in Conceptual Design, Modelling, and Decision-making: A Review. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2024, 10(4).

[9] Li, W. (2025). Research on Optimization of M&A Financial Due Diligence Process Based on Data Analysis. Journal of Computer, Signal, and System Research, 2(5), 115-121.

[10] Xu, H. (2025). Research on the Implementation Path of Resource Optimization and Sustainable Development of Supply Chain. International Journal of Humanities and Social Science, 1(2), 12-18.

[11] Li Y, Song X, Liu T M. GAPBAS: Genetic algorithm-based privacy budget allocation strategy in differential privacy K-means clustering algorithm. Computers & Security, 2024, 139(Apr. ): 103697. 1-103697. 12.

[12] Lu, Z. (2025). Design and Practice of AI Intelligent Mentor System for DevOps Education. European Journal of Education Science, 1(3), 25-31.

[13] Yu, X. (2025). Application Analysis of User Behavior Segmentation in Enhancing Customer Lifetime Value. Journal of Humanities, Arts and Social Science, 9(10).

[14] Zheng, H. (2025). Research on Lifecycle Configuration and Reclamation Strategies for Edge Nodes Based on Microservice Architectures. Advances in Computer and Communication, 6(5).

[15] Li, J. (2025). The Impact of Distributed Data Query Optimization on Large-Scale Data Processing.