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Dual Scheme Privacy-Preserving Approach for Location-Aware Application in Edge Computing

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Book cover Ad Hoc Networks and Tools for IT (ADHOCNETS 2021, TridentCom 2021)

Abstract

The location awareness capabilities of edge computing (EC) contains large quantity of the physical devices with short coverage range. The possibilities of the potential private data attacks from adversaries increases dramatically through easily accessible location information. The existing research on privacy-preserving schemes cannot meet various privacy-preserving expectations in practice for EC variants. In this paper, we proposed a dual scheme customizable \(\epsilon \)-differential privacy preservation to provide comprehensive protection. We establish the first scheme by clustering Edge Nodes (ENs) with SDN-enabled EC where SDN enables the capabilities of the programmability. In addition, we customize the \(\epsilon \)-differential privacy preservation scheme for variant EC services with the employment of modified Laplacian mechanism to generate noise, where the optimal tradeoff been found. The extensive experiments results demonstrate the significance of the proposed model in terms of privacy protection level and data utility, respectively.

Supported by Intelligent Technology Innovation Lab (ITIL), Victoria University.

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References

  1. Vicfreewifi access point locations - victorian government data directory, July 2017. https://discover.data.vic.gov.au/dataset/vicfreewifi-access-point-locations

  2. Badsha, S., et al.: Privacy preserving location-aware personalized web service recommendations. IEEE Trans. Serv. Comput. 14(3), 791–804 (2018)

    Article  Google Scholar 

  3. Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments. SCI, vol. 546, pp. 169–186. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05029-4_7

    Chapter  Google Scholar 

  4. Dang, T.D., Hoang, D.: A data protection model for fog computing. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pp. 32–38 (2017)

    Google Scholar 

  5. Deepali, Bhushan, K.: DDoS attack defense framework for cloud using fog computing. In: 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), pp. 534–538 (2017)

    Google Scholar 

  6. Frey, B.J., Dueck, D.: Affinity propagation (2007)

    Google Scholar 

  7. Xia, Q., Tao, Z., Li, Q.: Privacy issues in edge computing. In: Chang, W., Wu, J. (eds.) Fog/Edge Computing For Security, Privacy, and Applications. AIS, vol. 83, pp. 147–169. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57328-7_6

    Chapter  Google Scholar 

  8. Gu, B., Wang, X., Qu, Y., Jin, J., Xiang, Y., Gao, L.: Context-aware privacy preservation in a hierarchical fog computing system. In: 2019 IEEE International Conference on Communications (ICC), ICC 2019, pp. 1–6. IEEE (2019)

    Google Scholar 

  9. Gu, B.S., Gao, L., Wang, X., Qu, Y., Jin, J., Yu, S.: Privacy on the edge: customizable privacy-preserving context sharing in hierarchical edge computing. IEEE Trans. Netw. Sci. Eng. 7, 2298–2309 (2019)

    Article  MathSciNet  Google Scholar 

  10. Hasan, K., Ahmed, K., Biswas, K., Islam, M.S., Kayes, A.S.M., Islam, S.M.R.: Control plane optimisation for an SDN-based WBAN framework to support healthcare applications. Sensors 20(15) (2020). https://doi.org/10.3390/s20154200. https://www.mdpi.com/1424-8220/20/15/4200

  11. Hasan, K., Ahmed, K., Biswas, K., Saiful Islam, M., Ameri Sianaki, O.: Software-defined application-specific traffic management for wireless body area networks. Future Gener. Comput. Syst. 107, 274–285 (2020). https://doi.org/10.1016/j.future.2020.01.052, https://www.sciencedirect.com/science/article/pii/S0167739X19322587

  12. Ho, S., Qu, Y., Gu, B., Gao, L., Li, J., Xiang, Y.: DP-GAN: differentially private consecutive data publishing using generative adversarial nets. J. Netw. Comput. Appl. 185, 103066 (2021)

    Article  Google Scholar 

  13. Kang, J., Yu, R., Huang, X., Zhang, Y.: Privacy-preserved pseudonym scheme for fog computing supported internet of vehicles. IEEE Trans. Intell. Transp. Syst. 19(8), 2627–2637 (2018)

    Article  Google Scholar 

  14. Lu, R., Heung, K., Lashkari, A.H., Ghorbani, A.A.: A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access 5, 3302–3312 (2017)

    Article  Google Scholar 

  15. Lyu, L., Nandakumar, K., Rubinstein, B., Jin, J., Bedo, J., Palaniswami, M.: PPFA: privacy preserving fog-enabled aggregation in smart grid. IEEE Trans. Ind. Inf. 14(8), 3733–3744 (2018)

    Article  Google Scholar 

  16. Ma, L., Liu, X., Pei, Q., Xiang, Y.: Privacy-preserving reputation management for edge computing enhanced mobile crowdsensing. IEEE Trans. Serv. Comput. 79, 500–513 (2018). Part 2

    Google Scholar 

  17. Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 20(1), 416–464 (2018)

    Article  Google Scholar 

  18. Nafi, N.S., Ahmed, K., Gregory, M.A., Datta, M.: Software defined neighborhood area network for smart grid applications. Future Gener. Comput. Syst. 79, 500–513 (2018). https://doi.org/10.1016/j.future.2017.09.064, https://www.sciencedirect.com/science/article/pii/S0167739X17311007

  19. Ni, J., Zhang, K., Lin, X., Shen, X.: Securing fog computing for internet of things applications: challenges and solutions. IEEE Commun. Surv. Tutor. 20(1), 601–628 (2018)

    Article  Google Scholar 

  20. Qu, Y., Yu, S., Gao, L., Zhou, W., Peng, S.: A hybrid privacy protection scheme in cyber-physical social networks. IEEE Trans. Comput. Soc. Syst. 5(3), 773–784 (2018)

    Article  Google Scholar 

  21. Rasool, R.U., Ashraf, U., Ahmed, K., Wang, H., Rafique, W., Anwar, Z.: CyberPulse: a machine learning based link flooding attack mitigation system for software defined networks. IEEE Access 7, 34885–34899 (2019). https://doi.org/10.1109/ACCESS.2019.2904236

    Article  Google Scholar 

  22. Stojmenovic, I., Wen, S.: The fog computing paradigm: scenarios and security issues. In: 2014 Federated Conference on Computer Science and Information Systems, pp. 1–8, September 2014. https://doi.org/10.15439/2014F503

  23. Wang, Q., Chen, D., Zhang, N., Ding, Z., Qin, Z.: PCP: a privacy-preserving content-based publish subscribe scheme with differential privacy in fog computing. IEEE Access 5, 17962–17974 (2017). https://doi.org/10.1109/ACCESS.2017.2748956

    Article  Google Scholar 

  24. Wang, T., Zhou, J., Chen, X., Wang, G., Liu, A., Liu, Y.: A three-layer privacy preserving cloud storage scheme based on computational intelligence in fog computing. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 3–12 (2018)

    Article  Google Scholar 

  25. Wang, W., Zhang, Q.: Privacy preservation for context sensing on smartphone. IEEE/ACM Trans. Netw. 24(6), 3235–3247 (2016). https://doi.org/10.1109/TNET.2015.2512301

    Article  Google Scholar 

  26. Wibowo, F.X., Gregory, M.A., Ahmed, K., Gomez, K.M.: Multi-domain software defined networking: research status and challenges. J. Netw. Comput. Appl. 87, 32–45 (2017). https://doi.org/10.1016/j.jnca.2017.03.004, https://www.sciencedirect.com/science/article/pii/S1084804517300991

  27. Yannuzzi, M., Milito, R., Serral-Graci, R., Montero, D., Nemirovsky, M.: Key ingredients in an IoT recipe: fog computing, cloud computing, and more fog computing. In: 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 325–329 (2014)

    Google Scholar 

  28. Zhang, J., Feng, X., Liu, Z.: A grid-based clustering algorithm via load analysis for industrial internet of things. IEEE Access 6, 13117–13128 (2018)

    Article  Google Scholar 

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Correspondence to Bruce Gu .

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Gu, B., Qu, Y., Ahmed, K., Ye, W., Tan, C., Miao, Y. (2022). Dual Scheme Privacy-Preserving Approach for Location-Aware Application in Edge Computing. In: Bao, W., Yuan, X., Gao, L., Luan, T.H., Choi, D.B.J. (eds) Ad Hoc Networks and Tools for IT. ADHOCNETS TridentCom 2021 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-030-98005-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-98005-4_22

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