Comparative analysis of Protection of User's Privacy for Internet of Things Environment Using Machine Learning

CSE

The rapid proliferation of Internet of Things (IoT) devices has brought forth unprecedented connectivity and convenience, but it has also given rise to serious concerns regarding user privacy and data security. This study presents a comprehensive comparative analysis of various machine learning approaches utilized to safeguard user privacy within IoT environments. The research begins by identifying the prevailing privacy challenges posed by IoT devices, such as data breaches, unauthorized access, and user profiling. To address these challenges, a collection of machine learning techniques is investigated, including but not limited to, anomaly detection, federated learning, differential privacy, and secure multi-party computation. A large-scale experimental setup is deployed to evaluate the effectiveness of each machine learning method in protecting user privacy. Real-world IoT datasets are utilized to emulate diverse scenarios and simulate potential attacks. Key performance metrics, such as accuracy, false positives, false negatives, and computational overhead, are assessed to quantify the strengths and weaknesses of each approach. Furthermore, the study explores the trade-offs between privacy preservation and utility, analyzing how different machine learning methods can achieve varying levels of privacy without compromising the overall functionality of IoT devices. In conclusion, this research highlights the significance of employing machine learning techniques to address privacy concerns within the ever-expanding IoT ecosystem. By understanding the strengths and limitations of each approach, stakeholders can make informed decisions to strike a balance between privacy preservation and optimal IoT performance, ensuring a safer and more secure user experience in the Internet of Things

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