Machine Unlearning for Mobility Data: An Algorithmic Perspective

Abstract

This work addresses machine unlearning for trajectory data, sequences of spatiotemporal points representing movement. Motivated by growing privacy concerns and regulations like GDPR and CCPA, which grant users the right to request deletion of their personal data from trained models (the right to be forgotten), we propose TraceHiding, an algorithmic framework that removes the influence of specific trajectories without full model retraining. TraceHiding estimates the data point importance and applies gradient updates to reverse it proportionally. The framework includes: (i) Estimating data point importance, (ii) a teacher-student architecture, and (iii) a loss function using Importance Scores to compute reversal gradients. We evaluate TraceHiding on benchmark trajectory classification datasets. Results show it outperforms strong baselines and state-of-the-art unlearning methods (Bad-T, SCRUB, NegGrad, and NegGrad+), effectively removing deleted trajectory influence, preserving retained data performance, and improving efficiency over retraining. To our knowledge, this is the first machine unlearning approach designed specifically for trajectory data.