Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (3): 760-764.DOI: 10.11772/j.issn.1001-9081.2019071313

• Cyber security • Previous Articles     Next Articles

Location perturbation algorithm based on geo-indistinguishability of user’s region of interest

LUO Huiwen1,2, LONG Shigong1,2   

  1. 1. Guizhou Provincial Key Laboratory of Public Big Data(Guizhou University), Guiyang Guizhou 550000, China;
    2. College of Computer Science and Technology, Guizhou University, Guiyang Guizhou 550000, China
  • Received:2019-08-01 Revised:2019-09-23 Online:2020-03-10 Published:2019-09-29
  • Supported by:
    This work is partially supported by the Guizhou Science Foundation Qiankehe Major Special Project ([2018](3001)).


罗惠雯1,2, 龙士工1,2   

  1. 1. 贵州省公共大数据重点实验室(贵州大学), 贵阳 550000;
    2. 贵州大学 计算机科学与技术学院, 贵阳 550000
  • 通讯作者: 龙士工
  • 作者简介:罗惠雯(1992-),女,河南南阳人,硕士研究生,CCF会员,主要研究方向:差分隐私;龙士工(1967-),男,湖南省石门人,教授,博士,主要研究方向:信息安全、差分隐私。
  • 基金资助:

Abstract: To solve the problem of personal location privacy leakage under the rapid development of the Internet of Things (IoT) technology, a location perturbation algorithm of Geo-indistinguishability based on the Region Of Interest (GROI) was proposed. Firstly, a random noise satisfying planar Laplacian distribution was added to the real location of the user. Secondly, the approximate location was obtained by the discretization operation. Thirdly, the query results were sanitized based on the given Region Of Interest (ROI), and the query errors were further reduced while the availability of the mechanism remained unchanged. Finally, experiments were carried out on Google map queries to compare the proposed algorithm with the geo-indistinguishable location privacy protection algorithm. The results show that the proposed algorithm has the average error of query results reduced at least 2% compared to geo-indistinguishable algorithm within a 6.0 km retrieval range, and the accuracy of query results better than that of geo-indistinguishable algorithm while the privacy level is not degraded. Especially for close-range retrieval, the proposed algorithm can reduce the query error.

Key words: location privacy protection, geo-indistinguishability, differential privacy, Region Of Interest (ROI), perturbation mechanism

摘要: 随着物联网(IoT)技术的快速发展,针对个人位置隐私泄露的问题,提出了一种基于用户感兴趣区域的地理不可区分性(GROI)的位置扰动算法。首先,添加服从平面拉普拉斯分布的随机噪声到用户的真实位置上;然后,通过离散化操作得到近似位置;再次,根据给定的感兴趣区域(ROI)对查询结果进行清洗,在保证机制可用性程度不变的情况下,进一步减小查询误差;最后,在谷歌地图查询上进行了实验验证,与地理不可区分性位置隐私保护算法相比,设计的扰动算法能够在6.0 km的检索范围内,将查询结果的平均误差降低了至少2%,在隐私保护水平不低于地理不可区分性算法的前提下,所提算法的查询结果的准确性优于地理不可区分性算法,尤其针对近距离检索,该算法能够减小查询误差。

关键词: 位置隐私保护, 地理不可区分性, 差分隐私, 感兴趣区域, 扰动机制

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