LSTM-TRPS: Trajectory reconstruction protection strategy based on semantic information encoding

Abstract

Mobility data from IoT devices are vulnerable to attacks, and existing privacy methods often fail to defend against background knowledge adversaries. To mitigate this risk, a Long Short Term Memory based Trajectory Reconstruction Protection Strategy (LSTM-TRPS) is proposed to generate utility-preserving but unidentifiable synthetic trajectories. LSTM-TRPS is designed as a post-processing defense, maintaining essential mobility patterns for analytics while blocking reconstruction attempts. It consists of four modules: (1) Point of interest (POI) Semantic Annotation (PSA) for geo-temporal labeling; (2) Hasse Diagram-based Semantic Encoding (HDSE) to preserve hierarchical semantics; (3) Feature Embedding and Adaptive Matrix Combination (FEAMC) to fuse spatial temporal features; and (4) a Bi-LSTM generator to produce robust trajectories. Experiments on real-world mobility datasets show that LSTM-TRPS reduces Hausdorff distance by 12.7 %, improves temporal alignment by 20 %, and lowers privacy leakage by over 50 % under strict privacy budgets. It also achieves over 90 % POI retention and strong generalization across datasets, making it well suited for privacy-preserving trajectory publishing in IoT and smart mobility scenarios.