Balance confidentiality and publicity of vector data: A novel geometric accuracy reduction method

Public use and sharing of vector data while preserving its confidentiality is a critical challenge in geographic information security. Geometric accuracy reduction processing is commonly employed to process high-precision vector data containing a large amount of sensitive information into publicly available lower-precision data, which can balance the confidentiality and publicity of vector data. However, current research struggles to satisfy the security, controllability, and availability of processed data simultaneously. To address this issue, we propose a novel ellipsoid spatial mapping-based method for reducing the geometric accuracy of vector data. This method involves designing a model that utilizes Earth ellipsoid and spatial mapping techniques to safeguard vector data with irreversible offsets. We also develop offset variation functions for the x- and y-directions based on the model and predefined parameters, ensuring the availability and controllability of the processed data. Experimental results indicate that our method satisfies controllable accuracy reduction requirements, preserves the significant majority of shape and topology of the original data, and provides faster processing speeds compared to other methods. Our method ensures that processed data complies with confidentiality regulations, promoting the broader application of vector data. •We address geographic information security challenges by reducing the geometric accuracy of vector data through the proposed ellipsoid spatial mapping model.•The proposed method constructs an ellipsoid spatial mapping model and designs offset change functions based on the specified target reduction accuracy.•Comprehensive theoretical analysis and experiments have demonstrated that the proposed method effectively balances the confidentiality and public availability of vector data and prevents direct exposure of the original coordinates.