A Comparative Analysis of Privacy-Preserving Techniques in IoT-Driven Urban Health Monitoring
Abstract
This research paper conducts a comprehensive comparative analysis of privacy-preserving techniques employed in the context of Internet of Things (IoT)-driven urban health monitoring systems. As the integration of IoT technologies becomes pervasive in smart cities, concerns regarding the privacy and security of health-related data have gained prominence. The study evaluates various privacy-preserving approaches, including encryption, anonymization, and differential privacy, implemented in IoT-enabled urban health frameworks. Through an examination of their effectiveness, strengths, and limitations, the research aims to provide insights into best practices for safeguarding sensitive health information in urban environments. Key considerations include the trade-off between privacy and utility, the impact on data accuracy, and the usability of different techniques in real-world applications.
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