基于策略图的三维位置隐私发布算法研究

doi:10.3969/j.issn.1671-1122.2024.04.010

摘要/Abstract

摘要：

随着移动智能终端的普及，基于位置服务（Location-Based Services，LBS）的应用迎来了爆发式增长，高层室内建筑是位置服务的重要应用场景之一。然而现有的位置隐私保护算法大多适用于二维位置数据，面向大型室内三维场景的位置隐私保护研究尚且不足，并且缺乏可个性化定制的三维隐私策略。针对该问题，文章提出了一种基于策略图的三维位置隐私发布算法。首先，设计一种基于可定制策略图的位置隐私保护框架，可根据具体场景需求动态定制适合的隐私策略；其次，设计两种面向三维的差分隐私变体机制，结合定制策略图，实现三维场景下的位置隐私保护；最后，在三维数据集上进行仿真实验，实验结果表明，与其他三维位置隐私保护算法相比，文章所提算法具有更好的稳定性和效用性。

关键词: 高层室内场景, 三维位置隐私, 策略图, 差分隐私, 基于位置服务

Abstract:

With the popularization of mobile smart terminals, the application of location-based services has seen explosive growth, and high-rise indoor buildings are one of the important application scenarios of LBS. However, most of the existing location privacy protection algorithms are applicable to 2D location data. The research on location privacy protection for large indoor 3D scenes is still insufficient and lacks personalizable 3D privacy policies. To address this problem, this paper proposed a 3D-location privacy publishing algorithm based on policy graph. Firstly, a customizable policy graph-based location privacy protection framework was designed, which could dynamically customize suitable privacy policies according to specific scene requirements. Secondly, two 3D-oriented differential privacy variant mechanisms were designed in combination with customized policy graph to realize location privacy protection in 3D scenes. Finally, simulation experiments were conducted on 3D datasets. The results demonstrate that, compared to other 3D location privacy preserving algorithms, the proposed algorithm has better stability and utility.

Key words: high-rise indoor scenes, 3D-location privacy, policy graph, differential privacy, location-based services

中图分类号:

TP309

尹春勇, 贾续康. 基于策略图的三维位置隐私发布算法研究[J]. 信息网络安全, 2024, 24(4): 602-613.

YIN Chunyong, JIA Xukang. Research on 3D-Location Privacy Publishing Algorithm Based on Policy Graph[J]. Netinfo Security, 2024, 24(4): 602-613.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 24

[1]	GNSS and LBS Association of China. White Book on the Development of China's Satellite Navigation and Location Services Industry in 2023[R]. Beijing: GNSS and LBS Association of China, 2023.
	中国卫星导航定位协会. 2023年中国卫星导航与位置服务产业发展白皮书[R]. 北京: 中国卫星导航定位协会, 2023.
[2]	YING Bidi, NAYAK A. A Distributed Social-Aware Location Protection Method in Untrusted Vehicular Social Networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 6114-6124.
[3]	SUNG K, LEVINE B, ZHELEVA M. Protecting Location Privacy from Untrusted Wireless Service Providers[C]// ACM. Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York: ACM, 2020: 266-277.
[4]	SHEIKH M S, LIANG Jun, WANG Wensong. Security and Privacy in Vehicular Ad Hoc Network and Vehicle Cloud Computing: A Survey[J]. Wireless Communications & Mobile Computing, 2020, 2020(3): 1-25.
[5]	QIU Chenxi, SQUICCIARINI A C, PANG Ce, et al. Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing via Geo-Indistinguishability[J]. IEEE Transactions on Mobile Computing, 2022, 21(7): 2436-2450.
[6]	DWORK C. Differential Privacy[C]// Spinger. Proceedings of the 33rd International Colloquium on Automata, Languages and Programming. Heiderberg: Springer, 2006: 1-12.
[7]	DWORK C. Differential Privacy: A Survey of Results[C]// Springer. International Conference on Theory and Applications of Models of Computation. Heiderberg: Springer, 2008: 1-19.
[8]	ZHU Tianqing, LI Gang, ZHOU Wanlei, et al. Differential Privacy and Applications[M]. Heiderberg: Springer, 2017.
[9]	WANG Lu, MENG Xiaofeng. Location Privacy Preservation in Big Data Era: A Survey[J]. Journal of Software, 2014, 25(4): 693-712.
	王璐, 孟小峰. 位置大数据隐私保护研究综述[J]. 软件学报, 2014, 25(4):693-712.
[10]	ANDRÉS M E, BORDENABE N E, CHATZIKOKOLAKIS K, et al. Geo-Indistinguishability: Differential Privacy for Location-Based Systems[C]// ACM. Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security. New York: ACM, 2013: 901-914.
[11]	LUO Huiwen, ZHANG Haoming, LONG Shigong, et al. Enhancing Frequent Location Privacy-Preserving Strategy Based on Geo-Indistinguishability[J]. Multimedia Tools & Applications, 2021, 80(14): 21823-21841.
[12]	XU Chuan, DING Yingyi, LUO Li, et al. Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks[J]. Journal of Software, 2022, 33(2): 699-716.
	徐川, 丁颖祎, 罗丽, 等. 车联网中基于位置服务的个性化位置隐私保护[J]. 软件学报, 2022, 33(2):699-716.
[13]	QARDAJI W, YANG Weining, LI Ninghui. Differentially Private Grids for Geospatial Data[C]// IEEE. 2013 IEEE 29th International Conference on Data Engineering (ICDE). NewYork: IEEE, 2013: 757-768.
[14]	ZHANG Xiaojian, JIN Kaizhong, MENG Xiaofeng. Private Spatial Decomposition with Adaptive Grid[J]. Journal of Computer Research and Development, 2018, 55(6): 1143-1156.
	张啸剑, 金凯忠, 孟小峰. 基于自适应网格的隐私空间分割方法[J]. 计算机研究与发展, 2018, 55(6):1143-1156.
[15]	YAN Yan, SUN Zichao, MAHMOOD A, et al. Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering[J]. ISPRS International Journal of Geo-Information, 2022, 11(7): 404.
[16]	KUMAR R, DAWRA S. Simulation of 3D Privacy Preservation and Location Monitoring Approach[J]. International Research Journal of Engineering and Technology, 2016, 3(5): 1099-1103.
[17]	ZHU Yujia, ZHAI Lidong. Location Privacy in Buildings: A 3-Dimensional k-Anonymity Model[C]// IEEE. 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks. New York: IEEE, 2014: 195-200.
[18]	MIN Minghui, XIAO Liang, DING Jiahao, et al. 3D Geo-Indistinguishability for Indoor Location-Based Services[J]. IEEE Transactions on Wireless Communications, 2021, 21(7): 1-14.
[19]	LOHAN E S, SHUBINA V, NICULESCU D. Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications[J]. Sensors, 2022, 22(2): 1-20.
[20]	HE Xi, MACHANAVAJJHALA A, DING Bolin. Blowfish Privacy: Tuning Privacy-Utility Trade-offs Using Policies[C]// ACM. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2014: 1447-1458.
[21]	CAO Yang, XIAO Yonghui, TAKAGI S, et al. PGLP: Customizable and Rigorous Location Privacy through Policy Graph[C]// Springer. European Symposium on Research in Computer Security. Heiderberg: Springer, 2020: 655-676.
[22]	KANG Haiyan, JI Yuanrui. Research on Time-Serial Location Data Publication Based on Local Differential Privacy[J]. Acta Electronica Sinica, 2022, 50(9): 2222-2232. doi: 10.12263/DZXB.20210338
	康海燕, 冀源蕊. 基于本地化差分隐私的时序位置发布方案研究[J]. 电子学报, 2022, 50(9):2222-2232. doi: 10.12263/DZXB.20210338
[23]	XIAO Yonghui, XIONG Li. Protecting Locations with Differential Privacy Under Temporal Correlations[C]// ACM. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2015: 1298-1309.
[24]	HARDT M, TALWAR K. On the Geometry of Differential Privacy[C]// ACM. Proceedings of the Forty-Second ACM Symposium on Theory of Computing. New York: ACM, 2010: 705-714.