Data Privacy Enhancing in the IoT User/Device Behavior Analytics

Abstract

The Internet of Things (IoT) is generating and processing a huge amount of data that are then used and shared to improve services and applications in various industries. The collected data are always including sensitive information (sensitive data, users/devices/applications behaviors, etc.), which can be exchanged over the IoT to third-party for storing, processing, and sharing with associated applications. It is important to protect data privacy from compromising using consistently privacy preserving techniques. In this work, we propose a privacy-preserving solution for both structured data and unstructured data by using data anonymization techniques, which are able to enhance privacy associated with IoT services, applications, and users/device behavior. This can allow IoT users/devices to access privacy-enhanced data protecting sensitive data against re-identification risks. The experimental results demonstrate that the proposed solution can provide privacy-enhanced data for third-party services and applications over the IoT.