C-SHIFT:Efficient Cluster-based Model Fairness Control under Data Drift

This study introduces ALOG (Adaptive Longitudinal Grids for Geospatial Data using Local Differential Privacy), a novel framework designed to optimize geospatial data collection and frequency estimation while ensuring robust user privacy.ALOG leverages adaptive grids to dynamically adjust spatial granularity based on data density, eliminating the need for prior knowledge about data distribution.We evaluate ALOG and its variations using both synthetic and real-world datasets, comparing their effectiveness against state-of-the-art protocols.Experimental results demonstrate ALOG's superior performance in balancing privacy and utility, particularly under varying grid sizes and privacy budgets.The findings highlight the effectiveness of adaptive grid refinement in achieving precise frequency estimates in privacy-sensitive applications without relying on prior knowledge of data density.