Abstract
To provide privacy protection, task recommendation protocols for mobile crowdsourcing networks typically encrypt tasks before publishing them to the service provider. However, current task recommendation protocols are mainly focusing the privacy of user data and lacking the protection for users’ real identities, resulting in a lot of security issues. Moreover, current privacy-preserving protocols for mobile crowdsourcing networks are typically built on bilinear pairing, leading to high computation costs. To address the above issues, we propose a novel task recommendation protocol with privacy-preserving called TR-MCN. Similar to protocols of this field, TR-MCN can provide privacy-preserving features for mobile crowdsourcing networks. However, different from other well-known approaches, TR-MCN uses pseudonyms instead of real identities, which can provide privacy protection for users’ real identities. Moreover, to simplify the management of pseudonyms and reduce the computation cost of bilinear pairing, we introduce the Bloom filter technique to TR-MCN and design a novel signcryption algorithm, which is much more efficient than current protocols. By doing so, TR-MCN can achieve high efficiency while still satisfying required security requirements. Experimential results show that TR-MCN is feasible for real world applications.
Similar content being viewed by others
References
Alt F et al (2010) Location-based crowdsourcing: extending crowdsourcing to the real world. In: Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries. NCHCI’2010, pp 13–22. ACM
Aubry E et al (2014) Crowdout: a mobile crowdsourcing service for road safety in digital cities. In: The First International Workshop on Crowdsensing Methods, Techniques, and Applications. IWCMTA’2014. IEEE, pp 86–91
Bloom BH (1970) Space/time trade-offs in hash coding with allowable errors [j]. Commun ACM 13:422–426
Boutsis I et al (2014) On task assignment for real-time reliable crowdsourcing. In: 2014 IEEE 34th International Conference on Distributed Computing Systems. ICDCS’2014. IEEE, pp 1–10
Chatzimilioudis G et al (2012) Crowdsourcing with smartphones [j]. IEEE Internet Comput 16:36–44
Chatzimilioudis G et al (2013) Crowdsourcing for mobile data management. In: 2013 IEEE 14th International Conference on Mobile Data Management, ICMDM’2013. IEEE, pp 3–4
Chen X et al (2012) Crowdsourcing for on-street smart parking. In: Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications. DAIVNA’2012, ACM, pp 1–8
Christina D et al (2011) A survey on privacy in mobile participatory sensing applications [j]. J Syst Softw 84:1928–1946
Do N et al (2012) Crowdmac: a crowdsourcing system for mobile access. In: Proceedings of the 13th International Middleware Conference. IMC’2012. Springer-Verlag, pp 1–20
Faggiani A et al (2014) Smartphone-based crowdsourcing for network monitoring: opportunities, challenges, and a case study [j]. IEEE Commun Mag 52:106–113
Ganti RK et al (2011) Mobile crowdsensing: current state and future challenges [j]. IEEE Commun Mag 49:32–39
Goyal V et al (2006) Attribute-based encryption for fine-grained access control of encrypted data. In: Proceedings of the 13th ACM conference on Computer and communications security. CCS’2006. ACM, pp 89–98
Hyrax V et al (2012) Crowdsourcing mobile devices to develop proximity-based mobile clouds. Carnegie Mellon University
Kim KH et al (2014) Dyswis: crowdsourcing a home network diagnosis. In: 2014 23rd International Conference on Computer Communication and Networks. ICCCN’2014. IEEE, pp 1–10
Lu R et al (2013) Spoc: a secure and privacy-preserving opportunistic computing framework for mobile-healthcare emergency [j]. IEEE Trans Parallel Distrib Syst 24:614–624
Lynn B (2006) PBC Library manual 0.5.11. http://crypto.stanford.edu/pbc/manual/
Motta G et al (2014) City feed: a crowdsourcing system for city governance. In: 2014 IEEE 8th International Symposium on Service Oriented System Engineering. ISSOSE’2014. IEEE, pp 439–445
Nandan N et al (2014) Challenges in crowdsourcing real-time information for public transportation. In: 2014 IEEE 15th International Conference on Mobile Data Management. ICMDM’2014. IEEE, pp 67–72
Openssl.org (2013) Openssl-1.0.1e.tar.gz. http://www.openssl.org/source/
Parshotam K et al (2013) Crowd computing: A literature review and definition. In: Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference. CSITC’2013. ACM, pp 121–130
Salas OF et al (2013) Assessing internet video quality using crowdsourcing. In: Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia, Barcelona. IWCMB’2013. ACM, pp 23–28
Shi J et al (2014) Crowdsourcing access network spectrum allocation using smartphones. In: Proceedings of the 13th ACM Workshop on Hot Topics in Networks. HTN’2014. ACM, pp 1–7
Tamilin A et al (2012) Context-aware mobile crowdsourcing. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh. UCP’2012. ACM, pp 717–720
Vukovic M et al (2009) Crowdsourcing for enterprises. In: 2009 Congress on Services-I. CSI’2009. IEEE, pp 686–692
Wang Y et al (2016) An incentive mechanism with privacy protection in mobile crowdsourcing systems [j]. Comp Netw 102:157–171
Yan T et al (2011) CrowdPark: a crowdsourcing-based parking reservation system for mobile phones. University of Massachusetts at Amherst
Yang D et al (2012) Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: Proceedings of the 18th annual international conference on Mobile computing and networking. ICMCN’2012. ACM, pp 173–184
Yang K et al (2013) Dac-macs: effective data access control for multiauthority cloud storage systems [j]. IEEE Trans Inf Foren Security 8:1790–1801
Yang K et al (2015) Security and privacy in mobile crowdsourcing networks: challenges and opportunities [j]. IEEE Commun Mag 53:75–81
Yi CW et al (2015) Toward crowdsourcing-based road pavement monitoring by mobile sensing technologies [j]. IEEE Trans Intell Transp Syst 16:1905–1917
Zhou H et al (2013) Consub: Incentive-based content subscribing in selfish opportunistic mobile networks [j]. IEEE J Select Areas Commun Suppl 31:669–679
Acknowledgements
This paper is supported by the NSFC (No. 71402070, 61101088), the NSF of jiangsu province (No. BK20161099), and the Opening Project of Key Lab of Information Network Security of Ministry of Public Security (No. C16604).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wan, C., Phoha, V.V. & Huang, D. TR-MCN: light weight task recommendation for mobile crowdsourcing networks. J Ambient Intell Human Comput 9, 1027–1038 (2018). https://doi.org/10.1007/s12652-017-0505-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-017-0505-5