A Clustering <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-Anonymity Privacy-Preserving Method for Wearable IoT Devices

Abstract

Wearable technology is one of the greatest applications of the Internet of Things. The popularity of wearable devices has led to a massive scale of personal (user-specific) data. Generally, data holders (manufacturers) of wearable devices are willing to share these data with others to get benefits. However, significant privacy concerns would arise when sharing the data with the third party in an improper manner. In this paper, we first propose a specific threat model about the data sharing process of wearable devices’ data. Then we propose a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-anonymity method based on clustering to preserve privacy of wearable IoT devices’ data and guarantee the usability of the collected data. Experiment results demonstrate the effectiveness of the proposed method.