학술논문
Novel Hybrid Geometric Transformation Function to Safeguard LBS Data
Document Type
Conference
Author
Source
2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART) System Modeling & Advancement in Research Trends (SMART), 2023 12th International Conference on. :140-145 Dec, 2023
Subject
Language
ISSN
2767-7362
Abstract
In the quest to enhance location data privacy, this investigation embarked on evaluating various geometric transformations' efficacy and this process constructed a custom metric to evaluate safety. Notably, transformations such as RBF, Möbius, Spherical, Tessellation, and Affine were applied to location data sets and then subjected to synthetic adversarial inferences. Our findings show that transformation complexity doesn't guarantee enhanced privacy outright. Specifically, while the RBF and Möbius transformations exhibited high complexity, they were less adept at safeguarding data privacy in certain scenarios. In contrast, simpler transformations on the location data such as Affine surprisingly provided robust protection against synthetic adversaries. The study introduced a new “attack proneness severity” metric, offering insights into each transformation's vulnerability.