Real World Autonomous IoT Based Data Privacy Protection Using Machine Learning

Connecting everyday objects to the web (IoT)has rapidly grown what might be anticipated to continue expanding with an estimated By 2025, there will be 75 billion linked gadgets. This increase in IoT devices has led to concerns regarding data privacy and security. Autonomous IoT-based data privacy protection using machine learning (ML) has the potential to address these concerns.This research proposes a real-world application of autonomous IoT-based data privacy protection using ML. The system will use ML algorithms to analyze data and identify potential privacy breaches. It will then autonomously take appropriate actions to protect the data and prevent further breaches.The proposed research will use a mixed-methods approach, including surveys, case studies, and experiments, to collect data from IoT users, privacy experts, and ML researchers. The data collected will be used to evaluate the effectiveness of the proposed system in protecting data privacy and to identify any potential ethical concerns.The expected outcomes of this research are the development of an autonomous IoT-based data privacy protection system using ML, evaluation of its effectiveness in protecting data privacy, and identification of any ethical concerns that may arise.Overall, this research has the potential to contribute to the development of autonomous IoT-based data privacy protection systems that are effective in protecting data privacy while upholding ethical standards. The proposed Ultimately, this strategy might significantly alter the status quo of data privacy is protected in IoT, ensuring that users can confidently use IoT devices without concerns about privacy breaches.