Suppression techniques for privacy-preserving trajectory data publishing

被引：10
|
作者：
Lin, Chen-Yi [1 ]
机构：
[1] Natl Taichung Univ Sci & Technol, Dept Informat Management, Taichung, Taiwan
来源：
KNOWLEDGE-BASED SYSTEMS | 2020年 / 206卷
关键词：
Trajectories; Privacy protection; Indexing structures; Pruning strategies; ANONYMIZATION;
D O I：
10.1016/j.knosys.2020.106354
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we study the problem of protecting privacy in trajectory datasets from adversaries who can exploit their partial knowledge to infer unknown locations. To efficiently solve this problem, we propose a tree-based indexing structure to store all trajectory data and develop pruning strategies. We provide two algorithms to find a safe counterpart of the original trajectory dataset by using the pruning strategies. Finally, our experimental results demonstrate the efficiency of the proposed algorithms. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数：15
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