Part of the book series:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ((LNICST,volume 407))
Included in the following conference series:
957Accesses
Abstract
Due to the development of the Internet of Things, mobile crowdsensing has emerged as a promising pervasive sensing paradigm for online spatiotemporal data collection, by leveraging ubiquitous mobile devices. However, privacy leakage of device users is a crucial problem, especially when an untrusted central platform in mobile crowdsensing is considered. Moreover, private information of users like trajectories contained in both location tags and sensed values of their sensing data may be unexpectedly revealed to the platform. In order to solve this problem, we proposed a joint location-value privacy protection approach, which consists of two privacy preserving mechanisms to perturb the locations and sensed values of users, respectively. The approach can be performed by each user locally and independently. The privacy of users can be well preserved, as we theoretically prove that the two mechanisms satisfy local differential privacy. In addition, extensive simulations are conducted, and the results show that accurate estimated values can be derived based on perturbed locations and sanitized sensed values, by adopting the truth discovery method.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 10295
- Price includes VAT (Japan)
- Softcover Book
- JPY 12869
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abowd, J.M.: The US census Bureau adopts differential privacy. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 2867 (2018)
Dwork, C.: Differential privacy. In: Henk, C., van Tilborg, A., Jajodia, S. (eds.) Encyclopedia of Cryptography and Security pp. 338–340 (2011).https://doi.org/10.1007/978-1-4419-5906-5_752
Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci.9(3–4), 211–407 (2014)
Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag.49(11), 32–39 (2011)
Jin, H., Su, L., Ding, B., Nahrstedt, K., Borisov, N.: Enabling privacy-preserving incentives for mobile crowd sensing systems. In: 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), pp. 344–353. IEEE (2016)
Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. In: Advances in neural information processing systems, pp. 2879–2887 (2014)
Kouicem, D.E., Bouabdallah, A., Lakhlef, H.: Internet of things security: A top-down survey. Comput. Netw.141, 199–221 (2018)
Li, Q., Li, Y., Gao, J., Zhao, B., Fan, W., Han, J.: Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1187–1198. ACM (2014)
Li, Y., et al.: Towards differentially private truth discovery for crowd sensing systems. arXiv preprintarXiv:1810.04760 (2018)
Lin, B.C., Wu, S.H., Tsou, Y.T., Huang, Y.: PPDCA: privacy-preserving crowdsensing data collection and analysis with randomized response. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2018)
Lin, J., Yang, D., Li, M., Xu, J., Xue, G.: Frameworks for privacy-preserving mobile crowdsensing incentive mechanisms. IEEE Trans. Mob. Comput.17(8), 1851–1864 (2017)
Liu, L., Liu, W., Zheng, Y., Ma, H., Zhang, C.: Third-eye: a mobilephone-enabled crowdsensing system for air quality monitoring. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol.2(1), 20 (2018)
Ma, H., Zhao, D., Yuan, P.: Opportunities in mobile crowd sensing. IEEE Commun. Mag.52(8), 29–35 (2014)
Maisonneuve, N., Stevens, M., Niessen, M.E., Steels, L.: NoiseTube: measuring and mapping noise pollution with mobile phones. In: Athanasiadis, I.N., Rizzoli, A.E., Mitkas, P.A., Gómez, J.M. (eds.) Information Technologies in Environmental Engineering. Environmental Science and Engineering, pp. 215–228. Springer, Heidelberg (2009).https://doi.org/10.1007/978-3-540-88351-7_16
McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: FOCS, vol. 7, pp. 94–103 (2007)
Pournajaf, L., Garcia-Ulloa, D.A., Xiong, L., Sunderam, V.: Participant privacy in mobile crowd sensing task management: a survey of methods and challenges. ACM Sigmod Record44(4), 23–34 (2016)
Ren, X., et al.: LoPub: high-dimensional crowdsourced data publication with local differential privacy. IEEE Trans. Inf. Forensics Secur.13(9), 2151–2166 (2018)
To, H., Ghinita, G., Shahabi, C.: A framework for protecting worker location privacy in spatial crowdsourcing. Proc. VLDB Endow.7(10), 919–930 (2014)
Wang, L., Zhang, D., Yang, D., Lim, B.Y., Han, X., Ma, X.: Sparse mobile crowdsensing with differential and distortion location privacy. IEEE Trans. Inf. Forensics Secur.15, 2735–2749 (2020)
Wang, L., Zhang, D., Yang, D., Lim, B.Y., Ma, X.: Differential location privacy for sparse mobile crowdsensing. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1257–1262. IEEE (2016)
Wang, Q., Zhang, Y., Lu, X., Wang, Z., Qin, Z., Ren, K.: Real-time and spatio-temporal crowd-sourced social network data publishing with differential privacy. IEEE Trans. Dependable Secure Comput.15(4), 591–606 (2016)
Wang, Q., Zhang, Y., Lu, X., Wang, Z., Qin, Z., Ren, K.: RescueDP: real-time spatio-temporal crowd-sourced data publishing with differential privacy. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)
Wang, S., et al.: Local differential private data aggregation for discrete distribution estimation. IEEE Trans. Parallel Distrib. Syst.30, 2046–2059 (2019)
Wang, T., Zhao, J., Zhang, X., Yang, X.: A comprehensive survey on local differential privacy toward data statistics and analysis in crowdsensing. arXiv preprintarXiv:2010.05253 (2020)
Wang, Z., et al.: Personalized privacy-preserving task allocation for mobile crowdsensing. IEEE Trans. Mob. Comput.18(6), 1330–1341 (2018)
Yang, X., Wang, T., Ren, X., Yu, W.: Survey on improving data utility in differentially private sequential data publishing. IEEE Trans. Big Data7, 729–749 (2017)
Zhang, M., Yang, L., Gong, X., Zhang, J.: Privacy-preserving crowdsensing: privacy valuation, network effect, and profit maximization. In: 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2016)
Zhang, X., Yang, Z., Liu, Y.: Vehicle-based bi-objective crowdsourcing. IEEE Trans. Intell. Transp. Syst.99, 1–9 (2018)
Zhao, B., Rubinstein, B.I., Gemmell, J., Han, J.: A Bayesian approach to discovering truth from conflicting sources for data integration. Proc. VLDB Endow.5(6), 550–561 (2012)
Acknowledgements
This research is supported by Grant No. 61802245 from NSFC and Grant No. 20CG47 from Shanghai Chen Guang Program. We also appreciate the High Performance Computing Center of Shanghai University and Shanghai Engineering Research Center of Intelligent Computing System (No. 19DZ2252600) for providing the computing resources.
Author information
Authors and Affiliations
School of Computer Engineering and Science, Shanghai University, Shanghai, China
Tong Liu, Dan Li, Chenhong Cao, Honghao Gao & Chengfan Li
Shanghai Engineering Research Center of Intelligent Computing System, Shanghai, China
Tong Liu & Chengfan Li
School of Computer Science and Technology, Donghua University, Shanghai, China
Zhenni Feng
- Tong Liu
You can also search for this author inPubMed Google Scholar
- Dan Li
You can also search for this author inPubMed Google Scholar
- Chenhong Cao
You can also search for this author inPubMed Google Scholar
- Honghao Gao
You can also search for this author inPubMed Google Scholar
- Chengfan Li
You can also search for this author inPubMed Google Scholar
- Zhenni Feng
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toTong Liu.
Editor information
Editors and Affiliations
Shanghai University, Shanghai, China
Honghao Gao
Xi’an Jiaotong-Liverpool University, Suzhou, China
Xinheng Wang
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Liu, T., Li, D., Cao, C., Gao, H., Li, C., Feng, Z. (2021). Joint Location-Value Privacy Protection for Spatiotemporal Data Collection via Mobile Crowdsensing. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-030-92638-0_6
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-030-92637-3
Online ISBN:978-3-030-92638-0
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative