Skip to main navigation menu Skip to main content Skip to site footer

Vol. 1 No. 2 (2021)
Internet of Things and Edge Computing Journal

Articles

Privacy-Preserving IoT Data Management with Blockchain and AI - A Scholarly Examination of Decentralized Data Ownership and Access Control Mechanisms

Published 15-07-2021

  • Mohan Raparthi

Mohan Raparthi
Independent Researcher

Cover

Abstract

This paper explores the intersection of privacy-preserving techniques, blockchain technology, and artificial intelligence (AI) in managing Internet of Things (IoT) data. The proliferation of IoT devices has led to an exponential increase in the generation of sensitive data, raising concerns about privacy and security. Traditional centralized data management systems are often vulnerable to attacks and breaches. To address these challenges, this paper investigates the use of blockchain and AI to create decentralized data management systems that prioritize privacy and security. The study evaluates various decentralized data ownership and access control mechanisms, highlighting their effectiveness in enhancing privacy and security in IoT environments. Through a comprehensive analysis, this paper aims to provide insights into the benefits and challenges of implementing such systems and their potential impact on future IoT deployments.

Keywords

Privacy, IoT, blockchain, artificial intelligence, decentralized data management, access control, security, ownership, privacy-preserving techniques

PDF

References

  1. Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.
  2. Dorri A, Kanhere SS, Jurdak R, Gauravaram P. Blockchain for IoT security and privacy: The case study of a smart home. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops); 2017 Mar 13-17; Kona, HI, USA. p. 618-623. doi: 10.1109/PERCOMW.2017.7917597.
  3. Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
  4. Zyskind G, Nathan O, Pentland A. Decentralizing privacy: Using blockchain to protect personal data. In: Proceedings of the IEEE Security and Privacy Workshops; 2015 May 21-22; San Jose, CA, USA. p. 180-184. doi: 10.1109/SPW.2015.27.
  5. Pargaonkar, Shravan. "Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering." Journal of Science & Technology 1.1 (2020): 67-81.
  6. Li X, Jiang P, Chen T, Luo X, Wen Q, Jin D. A survey on the security of blockchain systems. Future Gener Comput Syst. 2019 Jun;97:512-529. doi: 10.1016/j.future.2018.12.025.
  7. Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.
  8. Dorri A, Steger M, Kanhere SS, Jurdak R. Blockchains for decentralized optimization of energy consumption in electric vehicles. IEEE Internet of Things J. 2019 Oct;6(5):8220-8231. doi: 10.1109/JIOT.2019.2926307.
  9. Pargaonkar, S. (2020). Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering. Journal of Science & Technology, 1(1), 61-66.
  10. Andoni M, Robu V, Flynn D, Abram S, Geach D, Jenkins D, McCallum P, Peacock A, Ingram D, Pickup L, Broby D. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew Sustain Energy Rev. 2019 Aug;100:143-174. doi: 10.1016/j.rser.2018.10.014.
  11. Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.
  12. Zheng Z, Xie S, Dai H, Chen X, Wang H. An overview of blockchain technology: Architecture, consensus, and future trends. IEEE Intell Syst. 2017 Jul-Aug;32(1):34-49. doi: 10.1109/MIS.2017.18.