Privacy-Preserving Data Collection and User Consent Management
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The rapid proliferation of Internet of Things (IoT) devices has revolution ized data collection, enabling smart environments, personalized services, and advanced analytics. However, this massive influx of granular, continuous data poses unprecedented threats to user privacy. Traditional security mechanisms often fall short in addressing the unique constraints and scale of IoT ecosystems. This chapter presents a comprehen sive framework for privacy-preserving data collection coupled with robust user consent management in IoT networks. We explore state-of-the-art techniques including differen tial privacy, k-anonymity, and cryptographic approaches tailored for resource-constrained devices. Furthermore, we propose a dynamic consent management architecture that em powers users with granular control over their data lifecycle. Through extensive simulations using a smart home IoT dataset, we evaluate the critical trade-off between data utility and privacy protection. The results demonstrate that our hybrid approach achieves a high level of privacy preservation while maintaining sufficient data utility for downstream applications, establishing a scalable paradigm for trustworthy IoT deployments
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SECURE AND SCALABLE INTERNET OF THINGS
