Leveraging Machine Learning for Privacy Preservation in the Internet of Things


  • Amrik Singh Department of Computer Science & Engineering, M.B.S. College of Engineering and Technology, Jammu, Jammu and Kashmir, INDIA
  • Sanjeev Singh Department of Electronics and Communication Engineering, M.B.S. College of Engineering and Technology, Jammu, Jammu and Kashmir, INDIA
  • Suresh Limkar Department of Artificial Intelligence & Data Science, AISSMS Institute of Information Technology, Pune, Maharashtra, INDIA


Machine Learning, privacy preservation, IoT security, supervised learning


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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How to Cite

Singh, A. ., Singh, S. ., & Limkar, S. . (2023). Leveraging Machine Learning for Privacy Preservation in the Internet of Things. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 382–396. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3498



Research Article

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