Skip to main content
SpringerLink
Log in

Mobile crowd-sensing context aware based fine-grained access control mode

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The present of smart mobile devices have provided unprecedented flexibility to humankind, with which people are able to access kinds of system resource through internet everywhere, including confidential data, nevertheless. While the traditional computing environment is always considered to be static and security-guarded, the context of mobile computing is much more variable, complex, and risk-hidden. To provide appropriate protection on mobile devices, we proposed a context-aware model combined with crowd-sensing paradigm to achieve fine-grained measurement of user’s current context. Corresponding to the context-aware model, we categorize the context by kinds of attributes and proposed Attribute-tree based Context-Aware Access Control model to protect user’s privacy and confidential information. The experimental result indicates that our proposed model is fine-grained, efficient and flexible to apply to different mobile platforms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Abowd G D, Dey A K, Brown P J et al (1999) Towards a better understanding of context and context-awareness. Handheld and ubiquitous computing. Springer Berlin Heidelberg, pp 304–307

  2. Aich S, Sural S, Majumdar A K (2007) Starbac: spatiotemporal role based access control. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 4804 LNCS, n PART 2. pp 1567–1582

  3. Azizyan M, Constandache I, Choudhury R R (2009) Surroundsense: mobile phone localization via ambience fingerprinting. Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM. pp 261–272

  4. Bauer L, Cranor L F, Reiter M K, Vaniea K (2007) Lessons learned from the deployment of a smartphone-based access-control system. In: Proceedings of the 3rd symposium on usable privacy and security. pp 64–75

  5. Bertino E, Bonatti PA, Ferrari E (2001) TRBAC: a temporal role-based access control model. ACM Trans Inf Syst Secur 4(3):191–233

    Article  Google Scholar 

  6. Bertino E, Catania B, Damiani M L, Perlasca P (2005) Geo-rbac: A spatially aware rbac. In: Proceedings of the tenth ACM symposium on access control models and technologies. pp 29–37

  7. Burges C J C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc

  8. Cai CZ, Han LY, Ji ZL, Chen YZ (2004) Enzyme family classification by support vector machines. Proteins Struct Funct Bioinform 55(1):66–76

    Article  Google Scholar 

  9. Chang CC, Lin CJ (2013) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm

  10. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  11. Damiani M L, Bertino E, Catania B, Perlasca P (2007) Geo-rbac: a spatially aware RBAC. ACM Trans Inform Syst Secur 10(1)

  12. Eisenman S B et al (2007) The bikenet mobile sensing system for cyclist experience mapping. SenSys’07 - Proceedings of the 5th ACM conference on embedded networked sensor systems. pp 87–101

  13. El-Maleh K, Samouelian A, Kabal P (1999) Frame level noise classification in mobile environments, ICASSP. IEEE Int Confer Acoust Speech Signal Process Proceed 1:237–240

    Google Scholar 

  14. Guo B, Yu Z, Zhou X, Zhang D (2014) From participatory sensing to mobile crowd sensing, 2014 I.E. international conference on pervasive computing and communication workshops, PERCOM WORKSHOPS 2014. pp 593–598

  15. Himberg J, Mäntyjärvi J, Korpipää P (2001) Using PCA and ICA for exploratory data analysis in situation awareness. IEEE international conference on multisensor fusion and integration for intelligent systems. pp 127–131

  16. Joshi J B D, Bertino E, Ghafoor A (2002) Temporal hierarchies and inheritance semantics for GTRBAC. In: Proceedings of the seventh ACM symposium on access control models and technologies. pp 74–83

  17. Langley P, Iba W, Thompson K (1992) An Analysis of Bayesian Classifiers. In the tenth national conference on artificial intelligence. AAAI Press and MIT Press, pp 223–228

  18. Long S, Kooper R, Abowd DG, Atkeson GC (1996) Rapid prototyping of mobile contextaware applications: the cyberguide case study. The 2nd ACM international conference on mobile computing and networking. pp 10–12

  19. Mohan P, Padmanabhan V, Ramjee R, Nericell (2008) Rich monitoring of road and traffic conditions using mobile smartphones, SenSys’08 - Proceedings of the 6th ACM Conference on Embedded Networked Sensor Systems. pp. 323–336

  20. Poslad S, Laamanen H, Malaka R, Nick A, Buckle P, Zipf A (2001) CRUMPET: creation of user-friendly mobile services personalised for tourism. In: Proceedings of 3G 2001 – Second Int.Conf. on 3G mobile communication technologies, London, UK, pp 26–29

  21. Press WH et al (2007) Numerical Recipes: The Art of Scientific Computing, 3rd edn. Cambridge University Press, New York, pp 883–899

    MATH  Google Scholar 

  22. Ray I, Kumar M, Yu L (2006) Lrbac: A Location-aware Role-based Access Control Model, vol 4332, Proceedings of the Second International Conference on Information Systems Security, LNCS. Springer, Heidelberg, pp 147–161

    Google Scholar 

  23. Salam F M, Erten G (1999) Sensor fusion by principal and independent component decomposition using neural networks. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. pp 211–215

  24. Shi J, Zhang R, Liu Y, Zhang Y (2010) PriSense: privacy-preserving data aggregation in people-centric urban sensing systems. 29th IEEE international conference on computer communications (INFOCOM). pp 758–766

  25. van Diggelen F (2009) A-GPS: Assisted GPS, GNSS, and SBAS. Artech House, London

    Google Scholar 

  26. Van Laerhoven K, Aidoo K, Lowette S (2001) Real-time analysis of data from many sensors with neural networks. International symposium on wearable computers, digest of papers. pp 115–122

  27. VanSetten M, Pokraev S, Koolwaaij J (2004) Context-Aware Recommendations in Mobile Tourist Application COMPASS. In: Nejdl, W & De Bra P (eds). AH 2004, LNCS 3137. Springer-Verlag

  28. Vapnik VN (1996) The nature of statistical learning theory, vol 38 n4. Springer Verlag, New York, p 400

    Google Scholar 

  29. Venanzi M et al (2014) Community-based bayesian aggregation models for crowdsourcing. WWW 2014 - Proceedings of the 23rd international conference on world wide web. pp 155–164

  30. Wang Z, Liao J, Cao Q, Qi H, Wang Z (2015) Friendbook: a semantic-based friend recommendation system for social networks. IEEE Trans Mob Comput 14(3):538–551

    Article  Google Scholar 

  31. Wang K, Liu, P X (2014) Adaptive multi-model and entropy-based localization on context-aware robotic system, on Systems, Man and Cybernetics (SMC), 2014 I.E. International Conference. pp 3755–3760

  32. Wirz M et al (2013) Using mobile technology and a participatory sensing approach for crowd monitoring and management during large-scale mass gatherings. Co-evolution of intelligent socio-technical systems understanding complex systems. pp 61–77

  33. Zhou Q et al (2012) A novel integrity-preserving privacy data aggregation in sensor networks. J Comput Inform Syst 8(20):8471–8478

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61272453, by the Natural Science Foundation of Hubei Province under Grant 2014CFB379.

Author information

Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan, 430072, China

    Dengpan Ye, Yuan Mei, Jixiang Zhu & Kun Ouyang

  2. School of Mathematics and Statistics, South Central University for Nationalities, Wuhan, 430074, China

    Yueyun Shang

Authors
  1. Dengpan Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yueyun Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jixiang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kun Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dengpan Ye.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, D., Mei, Y., Shang, Y. et al. Mobile crowd-sensing context aware based fine-grained access control mode. Multimed Tools Appl 75, 13977–13993 (2016). https://doi.org/10.1007/s11042-015-2693-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2693-3

Keywords

Search

Navigation