南通大学学报（自然科学版）2026，Vol.25Issue(1)：1-13,13.DOI:10.12194/j.ntu.20241126001
基于深度学习的语义编码虚假轨迹重构方法
DL-SESTR:a deep learning-based semantic encoding method for synthetic trajectory reconstruction
摘要
Abstract
Background knowledge attacks pose serious threats to trajectory privacy by exploiting prior knowledge to infer user behavior patterns.Existing trajectory reconstruction methods,however,suffer from two major limitations.First,they fail to address semantic location information leakage effectively,and their deep learning models lack opti-mization under noisy conditions,leading to inadequate reconstruction accuracy and weak semantic extraction.Second,their poor generalization ability hinders efficient reconstruction across heterogeneous datasets,thereby limiting the comprehensiveness of privacy protection.To address these limitations,this study proposes a deep learning-based se-mantic encoding method for synthetic trajectory reconstruction(DL-SESTR),a false trajectory reconstruction method based on semantic information encoding.The method integrates a bidirectional long short-term memory network(Bi-LSTM)with an attention mechanism to capture spatiotemporal dependencies and dynamically identify key trajectory points,thereby improving noise resistance.It also introduces a point-of-interest semantic annotation algorithm(PSA)that matches multi-source point-of-interest(POI)data efficiently to enhance annotation performance.Furthermore,a hierarchical semantic encoding algorithm based on the Hasse diagram(HDSE)is proposed,constructing a semantic sensitivity weight model to distinguish high-priority semantic information from noise.Experiments on the T-Drive and GeoLife datasets evaluated model performance across dense and sparse regions,varying privacy budgets,and day-and-night scenarios.DL-SESTR consistently outperforms baseline methods in balancing privacy protection and data utility:Hausdorff distance is reduced by 0.3％,dynamic time warping(DTW)efficiency improves by 1.2 times,and root mean square(RMS)improves by 1.18 times.Under a low privacy budget(ε=0.01),the method still achieves a 95％Euclidean distance reduction rate,demonstrating strong robustness and generalization ability.关键词
语义编码/隐私保护/轨迹重构/网络安全/哈斯偏序/深度学习Key words
semantic encoding/privacy protection/trajectory reconstruction/cybersecurity/Hasse partial order/deep learning分类
信息技术与安全科学引用本文复制引用
李同鑫,章静,胡昊泽,曾钻洋,廖华雄..基于深度学习的语义编码虚假轨迹重构方法[J].南通大学学报（自然科学版）,2026,25(1):1-13,13.基金项目
国家自然科学基金面上项目(62471139) （62471139）
福建省卫生健康重大科研项目(2021ZD01001) （2021ZD01001）
福建省医疗卫生中青年骨干人才科研培养项目(GY-H-24179) （GY-H-24179）
福建省教育科研单位专项经费项目(2022639) （2022639）
福建理工大学科研启动基金项目(GY-S24002) （GY-S24002）
