Collection scheme of location data based on local differential privacy

doi:10.16511/j.cnki.qhdxxb.2018.22.058

Download: PDF(887 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract Methods are needed to protect a person's privacy while monitoring their location. This paper presents a scheme for collecting location data based on local differential privacy. First, a multi-phase randomized response is used to collect the location data based on their local differential privacy. Then, the density of a certain section is estimated using the statistical method and expectation maximization (EM) to analyze the location data. The scheme guarantees that an untrustworthy data collector can still obtain the location statistics without direct access to the original data. Extensive tests verify that EM provides better privacy protection and better utility than the statistical method with limited location data. The results of the statistical method and EM are similar with abundant location data.

Keywords statistical learning local differential privacy location privacy data collection randomized response

Issue Date: 16 January 2019

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	GAO Zhiqiang
	CUI Xiaolong
	DU Bo
	ZHOU Sha
	YUAN Chen
	LI Ai

Cite this article:

GAO Zhiqiang,CUI Xiaolong,DU Bo, et al. Collection scheme of location data based on local differential privacy[J]. Journal of Tsinghua University(Science and Technology), 2019, 59(1): 23-27.

URL:

http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.22.058 OR http://jst.tsinghuajournals.com/EN/Y2019/V59/I1/23

[1] GAO Z Q, SUN Y X, CUI X L, et al. Privacy-preserving hybrid K-means[J]. International Journal of Data Warehousing and Mining (IJDWM), 2018, 14(2):17.
[2] JIANG H B, ZHAO P, WANG C. RobLoP:Towards robust privacy preserving against location dependent attacks in continuous LBS queries[J]. IEEE/ACM Transactions on Networking, 2018, 26(2):1018-1032.
[3] 高志强, 崔翛龙, 周沙, 等. 本地差分隐私保护及其应用[J]. 计算机工程与科学, 2018, 40(6):1029-1036. GAO Z Q, CUI X L, ZHOU S, et al. Local differential privacy protection and its applications[J]. Computer Engineering and Science, 2018, 40(6):1029-1036. (in Chinese)
[4] PHILIP R K. General data protection regulation (GDPR) and paediatric medical practice in Ireland:A personal reflection[J/OL]. Irish Journal of Medical Science, 2018. (2018-06-29). https://doi.org/10.1007/s11845-018-1857-3.
[5] WANG Y J, CAI Z P, TONG X R, et al. Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems[J]. Computer Networks, 2018, 135:32-43.
[6] GHINITA G. Privacy for location-based services[J]. Synthesis Lectures on Information Security Privacy & Trust, 2013, 4(1):1-85.
[7] SUN X X, WANG H, LI J Y, et al. Enhanced P-sensitive K-anonymity models for privacy preserving data publishing[J]. Transactions on Data Privacy, 2008, 1(2):53-66.
[8] ARDAGNA C A, CREMONINI M, DE CAPITANI DI VIMERCATI S, et al. An obfuscation-based approach for protecting location privacy[J]. IEEE Transactions on Dependable and Secure Computing, 2011, 8(1):13-27.
[9] GONG L M, LI S D, WU C Y, et al. Secure "ratio" computation and efficient protocol for general secure two-party comparison[J]. IEEE Access, 2018, 6:25532-25542.
[10] DWORK C, ROTHBLUM G N, VADHAN S. Boosting and differential privacy[C]//2010 IEEE 51st Annual Symposium on Foundations of Computer Science. Las Vegas, USA, 2010:51-60.
[11] DWORK C, POTTENGER R. Toward practicing privacy[J]. Journal of the American Medical Informatics Association, 2013, 20(1):102-108.
[12] DWORK C, MCSHERRY F, NISSIM K, et al. Calibrating noise to sensitivity in private data analysis[M]//HALEVI S, RABIN T. Theory of cryptography. Berlin, Germany:Springer, 2012, 3876:265-284.
[13] GAO Z Q, WANG Y T, DUAN Y Y, et al. Multi-level privacy preserving data publishing[J]. International Journal of Innovative Computing and Applications, 2018, 9(2):66-76.
[14] LI Y, YANG J, JI W. Local learning-based feature weighting with privacy preservation[J]. Neurocomputing, 2016, 174:1107-1115.
[15] FANTI G, PIHUR V, ERLINGSSON Ú. Building a RAPPOR with the unknown:Privacy-preserving learning of associations and data dictionaries[J]. Proceedings on Privacy Enhancing Technologies, 2016, 2016(3):41-61.
[16] TIAN X Y, TAYLOR J. Selective inference with a randomized response[J]. The Annals of Statistics, 2018, 46(2):679-710.
[17] ERLINGSSON Ú, PIHUR V, KOROLOVA A. RAPPOR:Randomized aggregatable privacy-preserving ordinal response[C]//Proceedings of 2014 ACM SIGSAC Conference on Computer and Communications Security. Scottsdale, USA:ACM, 2014:1054-1067.