A Novel Quadtree-Based Genetic Programming Search for Searchable Encryption Optimization

The encoding method of a searchable encryption can significantly impact the performance of a location-based alert system. While there were attempts to design searchable encryption manually, Gray Encoding is considered the most preferable method. However, if the alert zones are scattered unevenly, Gray Encoding fails to achieve token aggregation. In this research, a novel Quadtree-based Genetic Programming (Quadtree-GP) is proposed to iteratively identify superior searchable encryption candidates for the location-based alert system. Quadtree-GP can be effectively applied on customized requirements and different grid maps. Extensive experimental results show that Quadtree-GP is able to find searchable encryption candidates that outperform GP search, random search, and the baseline Gray Encoding in terms of user response time, token remaining percentage, and execution time.