research-article

PLP: Protecting Location Privacy Against Correlation Analyze Attack in Crowdsensing

Authors:

Shanfeng Zhang,

Yunhao LiuAuthors Info & Claims

IEEE Transactions on Mobile Computing, Volume 16, Issue 9

Pages 2588 - 2598

https://doi.org/10.1109/TMC.2016.2624732

Published: 01 September 2017 Publication History

Abstract

Crowdsensing applications require individuals to share local and personal sensing data with others to produce valuable knowledge and services. Meanwhile, it has raised concerns especially for location privacy. Users may wish to prevent privacy leak and publish as many non-sensitive contexts as possible. Simply suppressing sensitive contexts is vulnerable to the adversaries exploiting spatio-temporal correlations in the user's behavior. In this work, we present PLP, a crowdsensing scheme which preserves privacy while it maximizes the amount of data collection by filtering a user’s context stream. PLP leverages a conditional random field to model the spatio-temporal correlations among the contexts, and proposes a speed-up algorithm to learn the weaknesses in the correlations. Even if the adversaries are strong enough to know the filtering system and the weaknesses, PLP can still provably preserve privacy, with little computational cost for online operations. PLP is evaluated and validated over two real-world smartphone context traces of 34 users. The experimental results show that PLP efficiently protects privacy without sacrificing much utility.

References

[1]

J. Corburn, “Confronting the challenges in reconnecting urban planning and public health,” Amer. J. Public Health, vol. 94, no. 4, pp. 541–546, 2004.

[2]

S. Shah, F. Bao, C.-T. Lu, and I.-R. Chen, “CROWDSAFE: Crowd sourcing of crime incidents and safe routing on mobile devices,” in Proc. 19th ACM SIGSPATIAL Int. Conf. Advances Geographic Inf. Syst., 2011, pp. 521–524.

[3]

Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma, “Mining interesting locations and travel sequences from GPS trajectories,” in Proc. 18th Int. Conf. World Wide Web, 2009, pp. 791–800.

Digital Library

[4]

P. Zhou, Y. Zheng, and M. Li, “How long to wait?: Predicting bus arrival time with mobile phone based participatory sensing,” in Proc. 10th Int. Conf. Mobile Syst. Appl. Services, 2012, pp. 379–392.

Digital Library

[5]

L. Sweeney, “k-anonymity: A model for protecting privacy,” Int. J. Uncertainty Fuzziness Knowl.-Based Syst., vol. 10, pp. 557 –570, 2002.

Digital Library

[6]

M. Gruteser and D. Grunwald, “Anonymous usage of location-based services through spatial and temporal cloaking,” in Proc. 1st Int. Conf. Mobile Syst. Appl. Services, 2003, pp. 31 –42.

Digital Library

[7]

B. Wang, B. Li, and H. Li, “Gmatch: Secure and privacy-preserving group matching in social networks,” in Proc. Global Commun. Conf. , 2012, pp. 726–731.

[8]

Z. Yang, C. Wu, and Y. Liu, “Locating in fingerprint space: Wireless indoor localization with little human intervention,” in Proc. 18th Annu. Int. Conf. Mobile Comput. Netw., 2012, pp. 269–280.

[9]

C. Wu, Z. Yang, Y. Liu, and W. Xi, “WILL: Wireless indoor localization without site survey,” in Proc. IEEE INFOCOM, 2012, pp. 64 –72.

[10]

L. Zhang, X. Li, Y. Liu, and T. Jung, “Verifiable private multi-party computation: Ranging and ranking,” in Proc. IEEE INFOCOM, 2013, pp. 605–609.

[11]

Q. Li and G. Cao, “ Efficient and privacy-preserving data aggregation in mobile sensing,” in Proc. 20th IEEE Int. Conf. Netw. Protocols, 2012, pp. 1 –10.

[12]

G. Ghinita, M. L. Damiani, C. Silvestri, and E. Bertino, “Preventing velocity-based linkage attacks in location-aware applications,” in Proc. 17th ACM SIGSPATIAL Int. Conf. Advances Geographic Inf. Syst., 2009, pp. 246–255.

Digital Library

[13]

M. Götz, S. Nath, and J. Gehrke, “MASKIT: Privately releasing user context streams for personalized mobile applications,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2012, pp. 289–300.

[14]

J. Lafferty, A. McCallum, and F. Pereira, “ Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proc. 18th Int. Conf. Mach. Learn., 2001, pp. 282–289 .

[15]

C. Sutton and A. McCallum, “An introduction to conditional random fields for relational learning,” in Introduction to Statistical Relational Learning. Cambridge, MA, USA: MIT Press, 2007.

[16]

K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, “ Mondrian multidimensional k-anonymity,” in Proc. 22nd Int. Conf. Data Eng., 2006, Art. no. 25.

[17]

M. Azizyan, I. Constandache, and R. R. Choudhury, “ SurroundSense: Mobile phone localization via ambience fingerprinting,” in Proc. 15th Annu. Int. Conf. Mobile Comput. Netw., 2009, pp. 261–272.

[18]

I. Constandache, X. Bao, M. Azizyan, and R. R. Choudhury, “Did you see Bob?: Human localization using mobile phones,” in Proc. 16th Annu. Int. Conf. Mobile Comput. Netw. , 2010, pp. 149–160.

[19]

Z. Huang, W. Du, and B. Chen, “Deriving private information from randomized data,” in Proc. ACM SIGMOD Int. Conf. Manage. Data , 2005, pp. 37–48.

[20]

M. Li, S. Yu, N. Cao, and W. Lou, “Privacy-preserving distributed profile matching in proximity-based mobile social networks,” IEEE Trans. Wireless Commun., vol. 12, no. 5, pp. 2024–2033, May 2013.

[21]

C. Rottondi, G. Verticale, and A. Capone, “ Privacy-preserving smart metering with multiple data consumers,” Comput. Netw., vol. 57, no. 7, pp. 1699–1713, 2013.

Digital Library

[22]

R. Cramer, I. Damgård, and S. Dziembowski, “ On the complexity of verifiable secret sharing and multiparty computation,” in Proc. 32nd Annu. ACM Symp. Theory Comput., 2000, pp. 325–334.

[23]

H. Ahmadi, N. Pham, R. Ganti, T. Abdelzaher, S. Nath, and J. Han, “Privacy-aware regression modeling of participatory sensing data,” in Proc. 8th ACM Conf. Embedded Netw. Sensor Syst., 2010, pp. 99– 112.

Digital Library

[24]

E. Shi, T. Chan, E. G. Rieffel, R. Chow, and D. Song, “Privacy-preserving aggregation of time-series data,” in Proc. Annu. Netw. Distrib. Syst. Secur. Symp., 2011, Art. no. 4.

[25]

D. Wang, Y. He, E. Rundensteiner, and J. F. Naughton, “Utility-maximizing event stream suppression,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2013, pp. 589–600.

[26]

A. Parate, M.-C. Chiu, D. Ganesan, and B. M. Marlin, “Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing,” in Proc. 11th Annu. Int. Conf. Mobile Syst. Appl. Services, 2013, pp. 83–96 .

Digital Library

[27]

S. Chakraborty, C. Shen, K. R. Raghavan, Y. Shoukry, M. Millar, and M. B. Srivastava, “ipShield: A framework for enforcing context-aware privacy,” in Proc. 11th USENIX Symp. Netw. Syst. Des. Implementation, 2014, pp. 143 –156.

[28]

S. Chakraborty, N. Bitouze, M. B. Srivastava, and L. Dolecek, “Protecting data against unwanted inferences,” in Proc. IEEE Inf. Theory Workshop, 2013, pp. 1–5.

Cited By

Qiu GTang GLi CLuo LGuo DShen Y(2023)Differentiated Location Privacy Protection in Mobile Communication Services: A Survey from the Semantic Perception PerspectiveACM Computing Surveys10.1145/361758956:3(1-36)Online publication date: 5-Oct-2023
https://dl.acm.org/doi/10.1145/3617589
Montori FBedogni L(2023)Privacy preservation for spatio-temporal data in Mobile Crowdsensing scenariosPervasive and Mobile Computing10.1016/j.pmcj.2023.10175590:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.pmcj.2023.101755
Schwarz-Rüsch SBehlendorf MBecker MKudlek RMohamed HSchoenitz FJehl LKapitza RBellavista PZhang KGherbi ABagchi SPatiño MDi Modica GGascon-Samson J(2022)EventChainProceedings of the 23rd ACM/IFIP International Middleware Conference10.1145/3528535.3565243(174-187)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3528535.3565243
Show More Cited By

Recommendations

Protecting location privacy using location semantics
KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

As the use of mobile devices increases, a location-based service (LBS) becomes increasingly popular because it provides more convenient context-aware services. However, LBS introduces problematic issues for location privacy due to the nature of the ...
Protecting privacy in data release
Foundations of security analysis and design VI

The evolution of the Information and Communication Technology has radically changed our electronic lives, making information the key driver for today's society. Every action we perform requires the collection, elaboration, and dissemination of personal ...
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms

Continued advances in mobile networks and positioning technologies have created a strong market push for location-based applications. Examples include location-aware emergency response, location-based advertisement, and location-based entertainment. An ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Mobile Computing

IEEE Transactions on Mobile Computing Volume 16, Issue 9

Sept. 2017

274 pages

ISSN:1536-1233

Issue’s Table of Contents

1536-1233 © 2016 IEEE.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 September 2017

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 29 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Qiu GTang GLi CLuo LGuo DShen Y(2023)Differentiated Location Privacy Protection in Mobile Communication Services: A Survey from the Semantic Perception PerspectiveACM Computing Surveys10.1145/361758956:3(1-36)Online publication date: 5-Oct-2023
https://dl.acm.org/doi/10.1145/3617589
Montori FBedogni L(2023)Privacy preservation for spatio-temporal data in Mobile Crowdsensing scenariosPervasive and Mobile Computing10.1016/j.pmcj.2023.10175590:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.pmcj.2023.101755
Schwarz-Rüsch SBehlendorf MBecker MKudlek RMohamed HSchoenitz FJehl LKapitza RBellavista PZhang KGherbi ABagchi SPatiño MDi Modica GGascon-Samson J(2022)EventChainProceedings of the 23rd ACM/IFIP International Middleware Conference10.1145/3528535.3565243(174-187)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3528535.3565243
Ke CXiao FHuang ZXiao F(2022)A user requirements-oriented privacy policy self-adaption scheme in cloud computingFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-1182-x17:2Online publication date: 8-Aug-2022
https://dl.acm.org/doi/10.1007/s11704-022-1182-x
Yin JYang ZCao HLiu TZhou ZWu C(2019)A Survey on Bluetooth 5.0 and MeshACM Transactions on Sensor Networks10.1145/331768715:3(1-29)Online publication date: 30-May-2019
https://dl.acm.org/doi/10.1145/3317687
Chen JHe KYuan QChen MDu RXiang Y(2018)Blind Filtering at Third Parties: An Efficient Privacy-Preserving Framework for Location-Based ServicesIEEE Transactions on Mobile Computing10.1109/TMC.2018.281148117:11(2524-2535)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1109/TMC.2018.2811481
Gu JWang JYu ZShen K(2018)Walls Have Ears: Traffic-based Side-channel Attack in Video StreamingIEEE INFOCOM 2018 - IEEE Conference on Computer Communications10.1109/INFOCOM.2018.8486211(1538-1546)Online publication date: 16-Apr-2018
https://dl.acm.org/doi/10.1109/INFOCOM.2018.8486211
Guo YZhang HZhang LFang LLi F(2018)Incentive Mechanism for Cooperative Intrusion Detection: An Evolutionary Game ApproachComputational Science – ICCS 201810.1007/978-3-319-93698-7_7(83-97)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-93698-7_7

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents