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Efficient data management techniques based on hierarchical IoT privacy using block chains in cloud environments

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Abstract

As cloud services become so ubiquitous that they can be used anytime, anywhere, there is an increasing interest in IoT privacy protection. Manufacturers of small and medium-sized businesses that produce IT devices are increasingly using a variety of technologies to easily connect to other devices, with ease and affordability. However, the importance of protection against IoT privacy is growing as devices connected to the Internet do not know what information is transmitted by third parties. In this paper, we propose an IoT privacy protection technique in which users’ privacy-related elements are classified into block chains and non-block chains so that the third parties do not use the information used in the cloud environment maliciously, so that users’ privacy can be handled probabilistically by grouping them into service chains. The proposed model uses user privacy information as a block chain to handle identity attributes and access control policies so that IoT privacy information is not exposed to third parties. This process can be used to protect users’ privacy, regardless of the size or purpose of the cloud environment. In the proposed technique, IoT privacy was grouped into hierarchical multilevel forms, while efficiency was improved by an average of 11.1% using computational-intensive cryptographic policies. In addition, because the proposed technique has improved the accessibility of IoT privacy information over the existing one, it has reduced the overhead of cloud servers by more than 17.2%.

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References

  1. Choi JG, Noh BN (2011) Security technology research in cloud computing environment. J Secur Eng 8(3):371–384

    Google Scholar 

  2. Jeong YS (2015) An efficiency management scheme using big data of healthcare patients using puzzy AHP. J Digit Converg 13(4):227–233

    Article  Google Scholar 

  3. Jeong YS (2016) An efficient IoT healthcare service management model of location tracking sensor. J Digit Converg 14(3):261–267

    Article  Google Scholar 

  4. Echenauer L, Gligor VD (2002) A key-management scheme for distributed sensor networks. In: Proceedings of the 9th ACM Conference on Computer and Communications Security. pp 41–47

  5. Chan H, Perrig A, Song D (2003) Random key predistribution schemes for Sensor networks. In: Proceedings of the 2003 IEEE Symposium on Security and Privacy. pp 197–213

  6. Zhu S, Setia S, Jajodia S (2002) A distributed group key managemet protocol for ad hoc networks. George Mason University Press, Fairfax, pp 1–150

    Google Scholar 

  7. Khalili A, Katz J, Arbaugh WA (2003) Toward secure key distribution in truly ad-hoc networks. In: Proceedings of the 2003 Symposium on Applications and the Internet Workshops (SAINT’03 Workshops). pp 342–346

  8. Notra S, Siddiqi M, Gharakheili HH, Sivaraman V, Boreli R (2014) An experimental study of security and privacy risks with emerging household appliances. In: Proceedings of the 2014 IEEE Conference on Communications and Network Security (CNS). pp 79–84

  9. Sivaraman V, Chan D, Earl D, Boreli R (2016) Smart-phones attacking smart-homes. In: Proceedings of the 9th ACM Conference on Security and Privacy in Wireless and Mobile Networks. pp 195–200

  10. Chakravorty A, Wlodarczyk T, Rong C (2013) Privacy preserving data analytics for smart homes. In: Proceedings of the 2013 IEEE in Security and Privacy Workshops (SPW). pp 23–27

  11. Jayalakshmi P, Saravanan R (2019) Link stable routing with minimal delay nodes for MANETs. Int J Soc Humanist Comput 3(1):46–60

    Article  Google Scholar 

  12. Trattner C, Kappe F (2013) Social stream marketing on Facebook: a case study. Int J Soc Humanist Comput 2(1/2):86–103

    Article  Google Scholar 

  13. Jeong YS (2016) Measuring and analyzing WiMAX security adopt to wireless environment of U-healthcare. J Digit Converg 11(3):279–284

    Google Scholar 

  14. Singh A, Chatterjee K (2017) Cloud security issues and challenges: a survey. J Netw Comput Appl 79:88–115

    Article  Google Scholar 

  15. Singh S, Jeong YS, Park JH (2016) A survey on cloud computing security: issues, threats, and solutions. J Netw Comput Appl 75:200–222

    Article  Google Scholar 

  16. Shen H, Zhang M, Shen J (2017) Efficient privacy-preserving cube-data aggregation scheme for smart grids. IEEE Trans Inf Forens Secur 12(6):1369–1381

    Article  Google Scholar 

  17. Bao H, Lu R (2015) A new differentially private data aggregation with fault tolerance for smart grid communications. IEEE Internet Things J 2(3):248–258

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Ni J, Zhang K, Lin X, Shen XS (2019) Balancing security and efffciency for smart metering against misbehaving collectors. IEEE Trans Smart Grid 10(2):1225–1236

    Article  Google Scholar 

  20. Alghamdi WY, Wu H, Kanhere SS (2017) Reliable and secure end-to-end data aggregation using secret sharing in WSNs. In: Proceedings of the 2017 IEEE Wireless Communications and Networking Conference(WCNC). pp 1–6

  21. Qian J, Qiu F, Wu F, Ruan N, Chen G, Tang S (2017) Privacy preserving selective aggregation of online user behavior data. IEEE Trans Comput 66(2):326–338

    MathSciNet  MATH  Google Scholar 

  22. Memon RA, Li JP, Nazeer MI, Khan AN, Ahmed J (2019) DualFog-IoT: additional fog layer for solving blockchain integration problem in internet of things. IEEE Access 7:169073–169093

    Article  Google Scholar 

  23. Yao H, Mai T, Wang J, Ji Z, Jiang C, Qian Y (2019) Resource trading in blockchain-based industrial internet of things. IEEE Trans Ind Inform 15(6):3602–3609

    Article  Google Scholar 

  24. li R, Song T, Mei B, Li H, Cheg X, Sun L (2019) Blockchain for large-scale internet of things data storage and protection. IEEE Trans Serv Comput 12(5):762–771

    Article  Google Scholar 

  25. Li Z, Yang Z, Xie S (2019) Computing resource trading for edge-cloud-assisted internet of things. IEEE Trans Ind Inform 15(6):3661–3669

    Article  Google Scholar 

  26. Xu J, Xue K, Li S, Tian H (2019) Healthchain: a blockchain-based privacy preserving scheme for large-scale health data. IEEE Internet Things J 6(5):8770–8781

    Article  Google Scholar 

  27. Zhang J, Huang H, Wang X (2016) Resource provision algorithms in cloud computing: a survey. J Netw Comput Appl 64:23–42

    Article  Google Scholar 

  28. Varadharajan V, Tupakula U (2014) Security as a service model for cloud environment. IEEE Trans Netw Serv Manag 11(1):60–75

    Article  Google Scholar 

  29. Iera A, Morabito G, Atzori L (2016) The internet of things moves into the cloud. In: Proceedings of the 2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW). pp 191–191

  30. Saha HN, Mandal A, Sinha A (2017) Recent trends in the internet of things. In: Proceedings of the 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). pp 1–4

  31. Celesti A, Mulfari D, Fazio M, Villari M, Puliafito A (2016) Exploring container virtualization in IoT clouds. In: Proceedings of the 2016 IEEE International Conference on Smart Computing (SMARTCOMP). pp 1–6

  32. Dar KS, Taherkordi A, Eliassen F (2016) Enhancing dependability of cloud-based IoT services through virtualization. In: Proceedings of the 2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI). pp 106–116

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Acknowledgements

This work was supported by the BB21+ Project in 2019.

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Authors and Affiliations

  1. Department of Information and Communication Convergence Engineering, Mokwon University, Daejeon, 35349, Republic of Korea

    Yoon-Su Jeong

  2. Department of Information Security, Tongmyong University, Busan, 48520, Republic of Korea

    Dong-Ryool Kim & Seung-Soo Shin

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  1. Yoon-Su Jeong
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  2. Dong-Ryool Kim
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  3. Seung-Soo Shin
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Correspondence to Seung-Soo Shin.

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Jeong, YS., Kim, DR. & Shin, SS. Efficient data management techniques based on hierarchical IoT privacy using block chains in cloud environments. J Supercomput 77, 9810–9826 (2021). https://doi.org/10.1007/s11227-021-03653-3

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