A study On : Confidentiality Approach to Prevent Features Disclosure in IoT Situations

Authors

  • Frimpong Atta Junior Osei  Nanjing University of Information Science and Technology, Jiangsu, China
  • Sidique Gawusu  Nanjing University of Information Science and Technology, Jiangsu, China
  • Xuezhi Wen  School of Computer and Software, Nanjing University of Information Science and Technology, Jiangsu, China
  • Yu Zheng  School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Jiangsu, China
  • Daniel Appiah Kumah  Nanjing University of Information Science and Technology, Jiangsu, China

DOI:

https://doi.org//10.32628/CSEIT2063146

Keywords:

Confidentiality, Preserves Privacy, Networks, Internet Of Things, User Privacy, Derived Group.

Abstract

This paper proposes an approach which safeguards confidentiality to avoid disclosures of features within a multiple IoT situation, that is, a setup of objects in networks that communicate with each other. Two ideas derived from the theory of databases, namely k-anonymity and t-certitude, form our basis. They are used to cluster the objects to provide a unitary view of them and their characteristics. In fact, the use of anonymity and t-closeness robustly ensures privacy for derived groups. Furthermore, description of the object grouping scheme that preserves privacy, which represents the core of our approach was studied. Eventually, we illustrated the corresponding security model and analyzed the associated properties. The study also provided important advantages for the protection of user privacy in all those situations where knowledge of object features may help an attacker to obtain information about user habits and behavior. This study prevents not only the disclosure of information but also the divulgation of features. This is a major strength of our approach as malicious analyzes of the characteristics of objects can interfere with the privacy of people.

References

  1. C. Zhang, L. Zhu, C. Xu, K. Sharif, X. Du, and M. Guizani, "LPTD: Achieving lightweight and privacy-preserving truth discovery in CIoT," Future Generation Computer Systems, vol. 90, pp. 175-184, 2019.
  2. A. I. Khan and A. Al-Badi, "Open Source Machine Learning Frameworks for Industrial Internet of Things," Procedia Computer Science, vol. 170, pp. 571-577, 2020/01/01/ 2020.
  3. E. Curry, W. Derguech, S. Hasan, C. Kouroupetroglou, and U. ul Hassan, "A real-time linked dataspace for the internet of things: enabling “pay-as-you-go” data management in smart environments," Future Generation Computer Systems, vol. 90, pp. 405-422, 2019.
  4. G. Baldassarre, P. L. Giudice, L. Musarella, and D. Ursino, "A paradigm for the cooperation of objects belonging to different IoTs," in Proceedings of the 22nd International Database Engineering & Applications Symposium, 2018, pp. 157-164.
  5. K. Skiadopoulos, K. Oikonomou, M. Avlonitis, K. Giannakis, D. Kogias, and I. Stavrakakis, "Multiple and replicated random walkers analysis for service discovery in fog computing IoT environments," Ad Hoc Networks, vol. 93, p. 101893, 2019/10/01/ 2019.
  6. G. Baldassarre, P. L. Giudice, L. Musarella, and D. Ursino, "The MIoT paradigm: main features and an “ad-hoc” crawler," Future Generation Computer Systems, vol. 92, pp. 29-42, 2019.
  7. X. Lu, J. Liu, W. Qi, and Q. Dai, "Multiple-target tracking based on compressed sensing in the Internet of Things," Journal of Network and Computer Applications, vol. 122, pp. 16-23, 2018/11/15/ 2018.
  8. L. Atzori, A. Iera, G. Morabito, and M. Nitti, "The Social Internet of Things (SIoT) – When social networks meet the Internet of Things: Concept, architecture and network characterization," Computer Networks, vol. 56, pp. 3594-3608, 2012/11/14/ 2012.
  9. R. M.S, S. Pattar, R. Buyya, V. K.R, S. S. Iyengar, and L. M. Patnaik, "Social Internet of Things (SIoT): Foundations, thrust areas, systematic review and future directions," Computer Communications, vol. 139, pp. 32-57, 2019/05/01/ 2019.
  10. M. Aloqaily, S. Otoum, I. Al Ridhawi, and Y. Jararweh, "An intrusion detection system for connected vehicles in smart cities," Ad Hoc Networks, vol. 90, p. 101842, 2019.
  11. Y. Zhang, R. H. Deng, G. Han, and D. Zheng, "Secure smart health with privacy-aware aggregate authentication and access control in Internet of Things," Journal of Network and Computer Applications, vol. 123, pp. 89-100, 2018/12/01/ 2018.
  12. S. Munirathinam, "Chapter Six - Industry 4.0: Industrial Internet of Things (IIOT)," in Advances in Computers. vol. 117, P. Raj and P. Evangeline, Eds., ed: Elsevier, 2020, pp. 129-164.
  13. S. Sicari, A. Rizzardi, and A. Coen-Porisini, "5G in the Internet of Things era: an overview on security and privacy challenges," Computer Networks, p. 107345, 2020/06/02/ 2020.
  14. D. Mocrii, Y. Chen, and P. Musilek, "IoT-based smart homes: A review of system architecture, software, communications, privacy and security," Internet of Things, vol. 1-2, pp. 81-98, 2018/09/01/ 2018.
  15. S. M. Tahsien, H. Karimipour, and P. Spachos, "Machine learning based solutions for security of Internet of Things (IoT): A survey," Journal of Network and Computer Applications, vol. 161, p. 102630, 2020/07/01/ 2020.
  16. S. N. Mohanty, K. C. Ramya, S. S. Rani, D. Gupta, K. Shankar, S. K. Lakshmanaprabu, et al., "An efficient Lightweight integrated Blockchain (ELIB) model for IoT security and privacy," Future Generation Computer Systems, vol. 102, pp. 1027-1037, 2020/01/01/ 2020.
  17. B. K. Mohanta, D. Jena, U. Satapathy, and S. Patnaik, "Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology," Internet of Things, vol. 11, p. 100227, 2020/09/01/ 2020.
  18. M. U. Hassan, M. H. Rehmani, and J. Chen, "Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions," Future Generation Computer Systems, vol. 97, pp. 512-529, 2019/08/01/ 2019.
  19. A. Ouaddah, "Chapter Eight - A blockchain based access control framework for the security and privacy of IoT with strong anonymity unlinkability and intractability guarantees," in Advances in Computers. vol. 115, S. Kim, G. C. Deka, and P. Zhang, Eds., ed: Elsevier, 2019, pp. 211-258.
  20. H. HaddadPajouh, A. Dehghantanha, R. M. Parizi, M. Aledhari, and H. Karimipour, "A survey on internet of things security: Requirements, challenges, and solutions," Internet of Things, p. 100129, 2019/11/09/ 2019.
  21. R. Hou, G. Ren, C. Zhou, H. Yue, H. Liu, and J. Liu, "Analysis and research on network security and privacy security in ubiquitous electricity Internet of Things," Computer Communications, vol. 158, pp. 64-72, 2020/05/15/ 2020.
  22. F. Allhoff and A. Henschke, "The Internet of Things: Foundational ethical issues," Internet of Things, vol. 1-2, pp. 55-66, 2018/09/01/ 2018.
  23. G. K. M. Abomhara, "Security and privacy in the internet of things: Current status and open issues," Proc. of the International Conference on Privacy and Security in MMbile Systems, PRISMS’14, IEEE, Aalborg, Denmark,, pp. 1-8, 2014.
  24. A. A. A. Ari, O. K. Ngangmo, C. Titouna, O. Thiare, Kolyang, A. Mohamadou, et al., "Enabling privacy and security in Cloud of Things: Architecture, applications, security & privacy challenges," Applied Computing and Informatics, 2019/11/22/ 2019.
  25. D. Minoli, "Positioning of blockchain mechanisms in IOT-powered smart home systems: A gateway-based approach," Internet of Things, p. 100147, 2019/11/27/ 2019.
  26. R. Shrestha and S. Kim, "Chapter Ten - Integration of IoT with blockchain and homomorphic encryption: Challenging issues and opportunities," in Advances in Computers. vol. 115, S. Kim, G. C. Deka, and P. Zhang, Eds., ed: Elsevier, 2019, pp. 293-331.
  27. M. Amiri-Zarandi, R. A. Dara, and E. Fraser, "A Survey of Machine Learning-based Solutions to Protect Privacy in the Internet of Things," Computers & Security, p. 101921, 2020/06/01/ 2020.
  28. S. Zhang, X. Li, Z. Tan, T. Peng, and G. Wang, "A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services," Future Generation Computer Systems, vol. 94, pp. 40-50, 2019/05/01/ 2019.
  29. Y. Wang, Z. Cai, Z. Chi, X. Tong, and L. Li, "A differentially k-anonymity-based location privacy-preserving for mobile crowdsourcing systems," Procedia Computer Science, vol. 129, pp. 28-34, 2018/01/01/ 2018.
  30. T. L. N. Li, S. Venkatasubramanian, "t-closeness: Privacy beyond kanonymity and l-diversity," Proc. of the International Conference on Data Engineering, ICDE’07, IEEE, Istanbul, Turkey, pp. 106–115, 2007.
  31. M. Quwaider and Y. Shatnawi, "Congestion Control Model for Securing Internet of Things Data Flow," Ad Hoc Networks, p. 102160, 2020/05/19/ 2020.
  32. H. S. Trivedi and S. J. Patel, "Design of secure authentication protocol for dynamic user addition in distributed Internet-of-Things," Computer Networks, vol. 178, p. 107335, 2020/09/04/ 2020.
  33. H. Elazhary, "Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions," Journal of Network and Computer Applications, vol. 128, pp. 105-140, 2019/02/15/ 2019.
  34. A. Alnaied, M. Elbendak, and A. Bulbul, "An intelligent use of stemmer and morphology analysis for Arabic information retrieval," Egyptian Informatics Journal, 2020/03/07/ 2020.
  35. R. I. Meneguette, A. Boukerche, F. A. Silva, L. Villas, L. B. Ruiz, and A. A. F. Loureiro, "A novel self-adaptive content delivery protocol for vehicular networks," Ad Hoc Networks, vol. 73, pp. 1-13, 2018/05/01/ 2018.
  36. C. Borrego, J. Borrell, and S. Robles, "Efficient broadcast in opportunistic networks using optimal stopping theory," Ad Hoc Networks, vol. 88, pp. 5-17, 2019/05/15/ 2019.
  37. J. Sengupta, S. Ruj, and S. Das Bit, "A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT," Journal of Network and Computer Applications, vol. 149, p. 102481, 2020/01/01/ 2020.
  38. M. Ehara, H. Samejima, M. Yamanoshita, Y. Asada, Y. Shogaki, M. Yano, et al., "REDD+ engagement types preferred by Japanese private firms: The challenges and opportunities in relation to private sector participation," Forest Policy and Economics, vol. 106, p. 101945, 2019/09/01/ 2019.
  39. S. Nicolazzo, A. Nocera, D. Ursino, and L. Virgili, "A privacy-preserving approach to prevent feature disclosure in an IoT scenario," Future Generation Computer Systems, vol. 105, pp. 502-519, 2020/04/01/ 2020.
  40. S. Zapechnikov, "Privacy-Preserving Machine Learning as a Tool for Secure Personalized Information Services," Procedia Computer Science, vol. 169, pp. 393-399, 2020/01/01/ 2020.
  41. Y. Li, D. Yang, and X. Hu, "A differential privacy-based privacy-preserving data publishing algorithm for transit smart card data," Transportation Research Part C: Emerging Technologies, vol. 115, p. 102634, 2020/06/01/ 2020.
  42. P. Wang and H. Zhang, "Differential privacy for sparse classification learning," Neurocomputing, vol. 375, pp. 91-101, 2020/01/29/ 2020.
  43. J. Domingo-Ferrer and J. Soria-Comas, "From t-closeness to differential privacy and vice versa in data anonymization," Knowledge-Based Systems, vol. 74, pp. 151-158, 2015/01/01/ 2015.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-06-30

Issue

Volume 6, Issue 3, May-June-2020

Section

Research Articles

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How to Cite

[1]
Frimpong Atta Junior Osei, Sidique Gawusu, Xuezhi Wen, Yu Zheng, Daniel Appiah Kumah, " A study On : Confidentiality Approach to Prevent Features Disclosure in IoT Situations, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.616-632, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT2063146