Selective Encryption Algorithm for Vectoral Geographic Data under Feature Point Grouping Strategy

Abstract

Selective encryption technology is a data security protection method that can balance encryption security and efficiency. Currently, there is ongoing research on applying this technology to vector geographic data. However, existing algorithms often use random selection methods for choosing objects to be encrypted, which results in lower security. To address this problem, we proposed a selective encryption algorithm for vector geographic data based on feature point grouping strategy. The study includes four steps: (1) Based on the user-input encryption ratio, calculate the feature point grouping thresholds for each element in the data. (2) Extract feature point sets for each element based on the grouping threshold. (3) Sort and group the feature point sets, reducing the smallest encryption unit to group objects within individual elements. (4) To reduce encryption costs and enhance the algorithm's resistance to attacks, perform frequency domain coefficient encryption and spatial domain coordinate value encryption in a stepwise manner on the group objects, ultimately producing the encryption results. The experiment showed that: (1) The decrypted encrypted data maintains consistency with the original data coordinates, achieving lossless decryption. (2) The correlation between encrypted data and the original data is significantly disrupted, resulting in a high level of randomness and thereby ensuring the algorithm's robust security. (3) The key space is substantial and highly sensitive, capable of withstanding brute force attacks. (4) The algorithm significantly improves encryption efficiency. (5) The algorithm displays resilience against deletion attacks and noise attacks. In conclusions, the proposed method enhances encryption efficiency while upholding a high level of security, thus it is an efficient selective encryption algorithm for vector geographic data.