Leveraging Machine Learning for Privacy Preservation in the Internet of Things
Keywords:
Machine Learning, privacy preservation, IoT security, supervised learningAbstract
The use of ML approaches to reduce privacy threats in the collecting, transmission, and processing of IoT data is examined in this study. We explore a number of facets of this paradigm, beginning with the creation of strong privacy-preserving algorithms. In order to keep personally identifiable information private throughout the IoT's data lifecycle, ML algorithms can be used to anonymised, encrypt, and obfuscate sensitive data.In order to detect unauthorised access and potential threats to IoT networks, ML-driven anomaly detection and intrusion detection systems are crucial. ML models can distinguish between regular and suspect activity by continuously monitoring network traffic and device behaviour. This helps to protect user privacy.The difficulties and moral issues related to using ML to protect privacy in IoT are also covered in this abstract. It examines the trade-offs that must be made between data utility and privacy, emphasising the significance of finding a solution that satisfies both user preferences and legal requirements.
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Yuvaraj, N.; Lakpathi, M.; Mithun, T.P. Study and Analysis of Protection Scheme of Digital Substation Using IEC61850-9-2 Process Bus Technology (2019). Int. J. Electr. Eng. Technol. 2019, 10, 1–9.
Talwar, S.; Loiselle, E.; Lambert, D.; Boutin, W.; Lavallee, M.; Sarubbi, F. Digital Transformation of Substation through IEC61850 Standard. CIGRE Canada. 2019.
Elbez, G.; Keller, H.B.; Hagenmeyer, V. Authentication of GOOSE Messages under Timing Constraints in IEC 61850 Substations. In Proceedings of the 6th International Symposium for ICS and SCADA Cyber Security Research 2019 (ICS-CSR), Athens, Greece, 10–12 September 2019.
S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.
Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2951
Luyi, S.; Lang, S. A Threat Modeling Language for Substation Automation Systems; KTH, School of Electrical Engineering and Computer Science (EECS): Stockholm, Sweden, 2020. [
Khodabakhsh, A.; Yayilgan, S.Y.; Houmb, S.H.; Hurzuk, N.; Foros, J.; Istad, M. Cyber-security gaps in a digital substation: From sensors to SCADA. In Proceedings of the 9th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 8–11 June 2020; pp. 1–4.
Dalipi, F.; Yildirim, S. Security and Privacy Considerations for IoT Application on Smart Grids: Survey and Research Challenges. In Proceedings of the 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Vienna, Austria, 22–24 August 2016.
Chabridon, S.; Laborde, R.; Desprats, T.; Oglaza, A.; Marie, P.; Marquez, S.M. ,”A survey on addressing privacy together with quality of context for context management in the Internet of Things”, Ann. Telecommun.-Ann. Télécommun. 2014, 69, 47–62.
Dwivedi, A.D.; Singh, R.; Ghosh, U.; Mukkamala, R.R.; Tolba, A.; Said, O. Privacy preserving authentication system based on non-interactive zero-knowledge proof suitable for Internet of Things. J. Ambient. Intell. Humaniz. Comput. 2021, 13, 4639–4649.
Fu, X.; Wang, Y.; Yang, Y.; Postolache, O. Analysis on cascading reliability of edge-assisted Internet of Things. Reliab. Eng. Syst. Saf. 2022, 223, 108463.
Jonsdottir, G.; Wood, D.; Doshi, R. IoT network monitor. In Proceedings of the 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, UK, 3–5 November 2017; pp. 1–5.
Lally, G.; Sgandurra, D. Towards a framework for testing the security of IoT devices consistently. In Proceedings of the International Workshop on Emerging Technologies for Authorization and Authentication, Barcelona, Spain, 7 September 2018; pp. 88–102.
Dovom, E.M.; Azmoodeh, A.; Dehghantanha, A.; Newton, D.E.; Parizi, R.M.; Karimipour, H. Fuzzy pattern tree for edge malware detection and categorization in IoT. J. Syst. Archit. 2019, 97, 1–7.
Pan, Z.; Sheldon, J.; Mishra, P. Hardware-assisted malware detection using explainable machine learning. In Proceedings of the 2020 IEEE 38th International Conference on Computer Design (ICCD), Hartford, CT, USA, 18–21 October 2020; pp. 663–666.
Gibert, D.; Mateu, C.; Planes, J. The rise of machine learning for detection and classification of malware: Research developments, trends and challenges. J. Netw. Comput. Appl. 2020, 153, 102526.
Mahdavinejad, M.S.; Rezvan, M.; Barekatain, M.; Adibi, P.; Barnaghi, P.; Sheth, A.P. Machine learning for Internet of Things data analysis: A survey. Digit. Commun. Netw. 2018, 4, 161–175.
Chen, M.; Gündüz, D.; Huang, K.; Saad, W.; Bennis, M.; Feljan, A.V.; Poor, H.V. Distributed learning in wireless networks: Recent progress and future challenges. IEEE J. Sel. Areas Commun. 2021, 39, 3579–3605.
Kumar, J.S.; Patel, D.R. A survey on internet of things: Security and privacy issues. Int. J. Comput. Appl. 2014, 90, 11.
Lin, H.; Bergmann, N.W. IoT privacy and security challenges for smart home environments. Information 2016, 7, 44.
Borgohain, T.; Kumar, U.; Sanyal, S. Survey of security and privacy issues of internet of things. arXiv 2015, arXiv:1501.02211.
Lin, J.; Yu, W.; Zhang, N.; Yang, X.; Zhang, H.; Zhao, W. A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 2017, 4, 1125–1142.
Yang, Y.; Wu, L.; Yin, G.; Li, L.; Zhao, H. A survey on security and privacy issues in Internet-of-Things. IEEE Internet Things J. 2017, 4, 1250–1258.
Salman, T.; Jain, R. Networking protocols and standards for internet of things. In Internet of Things and Data Analytics Handbook; Wiley: Hoboken, NJ, USA, 2017; pp. 215–238.
El-Gendy, S.; Azer, M.A. Security Framework for Internet of Things (IoT). In Proceedings of the 2020 15th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, 15–16 December 2020; pp. 1–6.
Guan, Z.; Zhang, Y.; Wu, L.; Wu, J.; Li, J.; Ma, Y.; Hu, J. APPA: An anonymous and privacy preserving data aggregation scheme for fog-enhanced IoT. J. Netw. Comput. Appl. 2019, 125, 82–92.
Tonyali, S.; Akkaya, K.; Saputro, N.; Uluagac, A.S.; Nojoumian, M. Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled smart metering systems. Future Gener. Comput. Syst. 2018, 78, 547–557.
Su, D.; Cao, J.; Li, N.; Bertino, E.; Jin, H. Differentially private k-means clustering. In Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, New Orleans, LA, USA, 9–11 March 2016; pp. 26–37
Dwork, C. Differential Privacy. In Encyclopedia of Cryptography and Security; Springer: Berlin/Heidelberg, Germany, 2011.
Konečnỳ, J.; McMahan, H.B.; Yu, F.X.; Richtárik, P.; Suresh, A.T.; Bacon, D. Federated learning: Strategies for improving communication efficiency. arXiv 2016, arXiv:1610.05492.
Smith, V.; Chiang, C.K.; Sanjabi, M.; Talwalkar, A.S. Federated multi-task learning. Adv. Neural Inf. Process. Syst. 2017, 30, 1–11.
Karnik, M. P. ., & Kodavade, D. . (2023). Abstractive Summarization with Efficient Transformer Based Approach . International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 291–298. https://doi.org/10.17762/ijritcc.v11i4.6454
White, M., Hall, K., López, A., Muñoz, S., & Flores, A. Predictive Maintenance in Manufacturing: A Machine Learning Perspective. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/154
Timande, S., Dhabliya, D. Designing multi-cloud server for scalable and secure sharing over web (2019) International Journal of Psychosocial Rehabilitation, 23 (5), pp. 835-841.
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