A Machine Learning Approach to Prevention of Data Over-Collection in Smart Devices

The growing number of smart devices has led to excessive data collection, compromising user privacy and device efficiency. To mitigate this issue, we present a machine learning model that identifies and prevents unnecessary data collection in real-time. Our approach analyzes device usage patterns, data flow, and system metrics to predict and detect over-collection. Experimental evaluations demonstrate that our model reduces data collection by 30% on average, without impacting device functionality. This study contributes to the development of privacy-preserving smart devices, promoting data protection, and resource optimization in the IoT ecosystem. Our solution offers a scalable and adaptive framework for mitigating data over-collection, enhancing user trust, and ensuring responsible device managementcollaboration are essential for transforming diabetes management and enhancing the quality of life for patients globally.