基于k-泛化技术的时空数据个人隐私保护方法

doi:10.3969/j.issn.1000-5641.2017.05.016

华东师范大学学报(自然科学版) ›› 2017, Vol. 2017 ›› Issue (5): 174-185.doi: 10.3969/j.issn.1000-5641.2017.05.016

基于k-泛化技术的时空数据个人隐私保护方法

杨姿, 宁博, 李毅

大连海事大学信息科学技术学院, 辽宁大连 116026

收稿日期:2017-06-20 出版日期:2017-09-25 发布日期:2017-09-25
通讯作者: 宁博,男,副教授,硕士生导师,研究方向为数据管理,隐私保护.E-mail:ningbo@dlmu.edu.cn E-mail:ningbo@dlmu.edu.cn
作者简介:杨姿,女,硕士研究生,研究方向为时空数据隐私保护.E-mail:winni103@vip.qq.com
基金资助:
国家自然科学基金广东联合基金重点项目（U1401256）；辽宁省自然科学基金（201602094）

Privacy preserving method of spatio-temporal data based on k-generalization technology

YANG Zi, NING Bo, LI Yi

College of Information Science and Technology, Dalian Maritime University, Dalian Liaoning 116026, China

Received:2017-06-20 Online:2017-09-25 Published:2017-09-25

摘要/Abstract

摘要： 近些年来，基于位置系统的设备越来越多，从而导致用户的大量位置信息被移动设备获取并利用，从数据挖掘的角度来说，这些数据具有不可估量的价值，但从个人隐私方面来说却恰恰相反，每个人都不希望自己的信息被泄露和利用，从而引发了人们强烈的隐私关注.目前许多文献都提出了隐私保护技术来解决这个问题，概括来说是干扰、抑制和泛化几大类.为了对个人时空数据的隐私进行保护，本文提出了k-泛化的方法.对用户可能出现的点进行范围限定，更好地提高了数据的可用性；对泛化节点的选取要使得用户的安全性最高；考虑了多个敏感节点存在情况下的解决方案，并且出于提高数据效用的目的对多个敏感节点进行了优化.最后通过实验评估了算法的性能并且验证了算法保护个人隐私是有效的.

关键词: 隐私, 时空数据, 匿名, 泛化

Abstract: In recent years, more and more devices based on location system, resulting in a large amount of location information by the mobile device users to access and use, from the perspective of data mining, the data is of immeasurable value, but in terms of personal privacy, people don't want their information to be leaked and used to sparked strong privacy concerns. At present, many papers have proposed privacy protection technology to solve this problem. Generally speaking, there are several categories of interference, suppression and generalization. In order to protect the privacy of personal spatio-temporal data, this paper proposes a method of k-generalization. To limit the scope of the user may appear, improve the availability of data; selection of nodes to generalization so that the user's maximum security; considers multiple sensitive node solutions exist under the condition, and for the purpose of improving the data utility on a number of sensitive nodes are optimized. Finally, the performance of the algorithm is evaluated by experiments, and it is proved that the algorithm is effective to protect personal privacy.

Key words: privacy, spatio-temporal data, anonymous, generalization

中图分类号:

TP391

杨姿, 宁博, 李毅. 基于k-泛化技术的时空数据个人隐私保护方法[J]. 华东师范大学学报(自然科学版), 2017, 2017(5): 174-185.

YANG Zi, NING Bo, LI Yi. Privacy preserving method of spatio-temporal data based on k-generalization technology[J]. Journal of East China Normal University(Natural Sc, 2017, 2017(5): 174-185.

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基于k-泛化技术的时空数据个人隐私保护方法

Privacy preserving method of spatio-temporal data based on k-generalization technology

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