A New Fast Framework for Anonymizing IoT Stream Data

The Internet of Things (IoT) plays an important role in human life today. Millions of devices generate and transmit vast amounts of data. Exploring this data without compromising privacy practices may expose to risk of users' identities. One of the measures used to protect data privacy is anonymity methods. IoT data anonymization is not possible using traditional methods because such data, unlike database data, are not static and are very large. In this paper, we propose a new framework that can anonymize the received stream data by considering their expiration time. This anonymization is performed using a new clustering method using a streaming data processing engine. The introduced clustering method has a significant effect on reducing data delay. It supports both numerical and categorical data types too. Also, merging remaining clusters at the end of the method has minimized information loss. Comparing the performance results of the introduced method with similar methods shows that the proposed method performs better in terms of information loss and data delay.