BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing

doi:10.12142/ZTECOM.202102004

ZTE Communications ›› 2021, Vol. 19 ›› Issue (2): 20-28.DOI: 10.12142/ZTECOM.202102004

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BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing

LIU Junyu, YANG Yongjian, WANG En()

Jilin University, Changchun 130012, China

Received:2021-03-11 Online:2021-06-25 Published:2021-07-27
About author:LIU Junyu received his bachelor’s degree in computer science and technology from Jilin University, China in 2019. Currently, he is pursuing for the master’s degree in computer science and technology at Jilin University. His current research interests include mobile crowdsensing and privacy preserving in mobile computing.|YANG Yongjian received his B.E. degree in automatization from Jilin University of Technology, China in 1983, M.E. degree in computer communication from Beijing University of Post and Telecommunications, China in 1991, and Ph.D. in software and theory of computer from Jilin University, China in 2005. He is currently a professor and a Ph.D. supervisor at Jilin University, Director of Key lab under the Ministry of Information Industry, and Standing Director of the Communication Academy. His research interests include network intelligence management, wireless mobile communication and services, and wireless mobile communication.|WANG En (wangen@jlu.edu.cn) received his B.E. degree in software engineering from Jilin University, China in 2011, and his M.E. degree and Ph.D. in computer science and technology from Jilin University in 2013 and 2016. He is currently an associate professor in the Department of Computer Science and Technology, Jilin University. His current research interests include the efficient utilization of network resources, scheduling and drop strategy in terms of buffer-management, energy-efficient communication between human-carried devices, and mobile crowdsensing.

Abstract

Abstract:

With the emergence of mobile crowdsensing (MCS), merchants can use their mobile devices to collect data that customers are interested in. Now there are many mobile crowdsensing platforms in the market, such as Gigwalk, Uber and Checkpoint, which publish and select the right workers to complete the task of some specific locations (for example, taking photos to collect the price of goods in a shopping mall). In mobile crowdsensing, in order to select the right workers, the platform needs the actual location information of workers and tasks, which poses a risk to the location privacy of workers and tasks. In this paper, we study privacy protection in MCS. The main challenge is to assign the most suitable worker to a task without knowing the task and the actual location of the worker. We propose a bilateral privacy protection framework based on matrix multiplication, which can protect the location privacy between the task and the worker, and keep their relative distance unchanged.

Key words: mobile crowdsensing, task allocation, privacy preserving

LIU Junyu, YANG Yongjian, WANG En. BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing[J]. ZTE Communications, 2021, 19(2): 20-28.

Figures/Tables 8

Table 1 Notation List

Notations	Description
w	A worker
t	A task
L_w	A matrix to represent the current location of a user
L_t	A matrix to represent the sensing area of a task
R_w	Confusion matrix of workers
R_T	Confusion matrix of task requester
k	The size of the location matrix
α	The matrix assignment variable
K_#	Task encryption key
SOD (R)	Sum of diagonal elements of matrix R

Figure 1 An example of generating privacy-preserving location matrix

Figure 2 Completed tasks vs. number of tasks

Figure 3 Completed tasks vs. number of workers

Figure 4 Travel distance vs. number of tasks

Figure 5 Travel distance vs. number of workers

Figure 6 Sum of diagonal elements of result matrix R

Figure 7 Time vs. dimension of the matrix and number of workers

References 23

1	LUO T, HUANG J W, KANHERE S S, et al. Improving IoT data quality in mobile crowd sensing: a cross validation approach [J]. IEEE Internet of Things journal, 2019, 6(3): 5651–5664. DOI: 10.1109/JIOT.2019.2904704 DOI URL
2	GANTI R K, YE F, LEI H. Mobile crowdsensing: current state and future challenges [J]. IEEE communications magazine, 2011, 49(11): 32–39. DOI: 10.1109/MCOM.2011.6069707 DOI URL
3	LIU Y T, KONG L H, CHEN G H. Data⁃oriented mobile crowdsensing: A comprehensive survey [J]. IEEE communications surveys & tutorials, 2019, 21(3): 2849–2885. DOI: 10.1109/COMST.2019.2910855 DOI URL
4	WANG L Y, ZHANG D Q, YANG D Q, et al. Sparse mobile crowdsensing with differential and distortion location privacy [J]. IEEE transactions on information forensics and security, 2020, 15: 2735–2749. DOI: 10.1109/TIFS.2020.2975925 DOI URL
5	XIAO M J, GAO G J, WU J, et al. Privacy⁃preserving user recruitment protocol for mobile crowdsensing [J]. IEEE/ACM transactions on networking, 2020, 28(2): 519–532. DOI: 10.1109/TNET.2019.2962362 DOI URL
6	ZHANG Y H, LI M, YANG D J, et al. Tradeoff between location quality and privacy in crowdsensing: An optimization perspective [J]. IEEE Internet of Things journal, 2020, 7(4): 3535–3544. DOI: 10.1109/JIOT.2020.2972555 DOI URL
7	ZENG B, YAN X F, ZHANG X L, et al. BRAKE: bilateral privacy⁃preserving and accurate task assignment in fog⁃assisted mobile crowdsensing [J]. IEEE systems journal, 9278, (99): 1–12. DOI: 10.1109/JSYST.2020.3009278 DOI URL
8	GAO G J, XIAO M J, WU J, et al. DPDT: A differentially private crowd⁃sensed data trading mechanism [J]. IEEE Internet of Things journal, 2020, 7(1): 751–762. DOI: 10.1109/JIOT.2019.2944107 DOI URL
9	SONG S W, LIU Z D, LI Z J, et al. Coverage⁃oriented task assignment for mobile crowdsensing [J]. IEEE Internet of Things journal, 2020, 7(8): 7407–7418. DOI: 10.1109/JIOT.2020.2984826 DOI URL
10	WANG T, XIE X K, CAO X, et al. On efficient and scalable time⁃continuous spatial crowdsourcing: full version [EB/OL]. (2020⁃10⁃29) [2021⁃01⁃25].
11	AKTER S, YOON S. Location⁃aware task assignment and routing in mobile crowd sensing [C]//2020 International Conference on Information and Communication Technology Convergence (ICTC). Jeju, Korea (South): IEEE, 2020: 51–53. DOI: 10.1109/ICTC49870.2020.9289316 DOI URL
12	LIU Y, GUO B, WANG Y, et al. TaskMe: multi⁃task allocation in mobile crowd sensing[C]//Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Heidelberg, Germany: ACM, 2016: 403–414. DOI: 10.1145/2971648.2971709 DOI URL
13	GUO B, LIU Y, WU W L, et al. ActiveCrowd: A framework for optimized multitask allocation in mobile crowdsensing systems [J]. IEEE transactions on human⁃machine systems, 2017, 47(3): 392–403. DOI: 10.1109/THMS.2016.2599489 DOI URL
14	WANG L Y, YANG D Q, HAN X, et al. Mobile crowdsourcing task allocation with differential⁃and⁃distortion geoobfuscation [J]. IEEE transactions on dependable and secure computing, 2021, 18(2): 967–981. DOI: 10.1109/TDSC.2019.2912886 DOI URL
15	WANG Z B, HU J H, LV R, et al. Personalized privacy⁃preserving task allocation for mobile crowdsensing [J]. IEEE transactions on mobile computing, 2019, 18(6): 1330–1341. DOI: 10.1109/TMC.2018.2861393 DOI URL
16	WANG X, LIU Z, TIAN X H, et al. Incentivizing crowdsensing with location⁃privacy preserving [J]. IEEE transactions on wireless communications, 2017, 16(10): 6940–6952. DOI: 10.1109/TWC.2017.2734758 DOI URL
17	NI J B, ZHANG K, XIA Q, et al. Enabling strong privacy preservation and accurate task allocation for mobile crowdsensing [J]. IEEE transactions on mobile computing, 2020, 19(6): 1317–1331. DOI: 10.1109/TMC.2019.2908638 DOI URL
18	YUAN D, LI Q, LI G L, et al. PriRadar: A privacy⁃preserving framework for spatial crowdsourcing [J]. IEEE transactions on information forensics and security, 2020, 15: 299–314. DOI: 10.1109/TIFS.2019.2913232 DOI URL
19	YAN K, LUO G C, ZHENG X, et al. A comprehensive location⁃privacy⁃awareness task selection mechanism in mobile crowd⁃sensing [J]. IEEE access, 2019, 7: 77541–77554. DOI: 10.1109/ACCESS.2019.2921274 DOI URL
20	GISDAKIS S, GIANNETSOS T, PAPADIMITRATOS P. Security, privacy, and incentive provision for mobile crowd sensing systems [J]. IEEE Internet of Things journal, 2016, 3(5): 839–853. DOI: 10.1109/JIOT.2016.2560768 DOI URL
21	ZHUO G Q, JIA Q, GUO L K, et al. Privacy⁃preserving verifiable data aggregation and analysis for cloud⁃assisted mobile crowdsourcing[C]//IEEE INFOCOM 2016 ⁃ The 35th Annual IEEE International Conference on Computer Communications. San Francisco, USA: IEEE, 2016: 1–9. DOI: 10.1109/INFOCOM.2016.7524547 DOI URL
22	ZHANG D S, ZHAO J J, ZHANG F, et al. Feeder: supporting last⁃mile transit with extreme⁃scale urban infrastructure data [C]//Proceedings of the 14th International Conference on Information Processing in Sensor Networks. Seattle, USA: IPSN, 2015: 226–237. DOI: 10.1145/2737095.2737121 DOI URL
23	ZHENG Y, ZHANG L Z, XIE X, et al. Mining interesting locations and travel sequences from GPS trajectories [C]//Proceedings of the 18th international conference on World Wide Web. Madrid, Spain: ACM, 2009: 791–800. DOI: 10.1145/1526709.1526816 DOI URL

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BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing

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