Privacy Preserving Wearable Data Publishing Using Inter Cloud Infrastructure

Monitoring health fitness is important to enjoy healthier life. Fitness trackers are wearable devices that use sensors to track body movements. The wearable device collect the data, converts it into steps, calories, sleep quality and general activity that one performs throughout the day. Apart from the health related data the devices also collect multiple sensitive attribute information like health history, GPS tracking and blood-oxygen levels. This collected data is published for various reasons like carrying out analysis on the data, for the purpose of the research, to learn stress levels, habits, and wellbeing, sleep and exercise patterns and to train the model. Among the constructive usage of the published data there may be an intruder who sells the data for monetary purpose. This causes privacy threats to the data owners. The device data is stored in cloud that causes different types of threats. Therefore, it is very much essential to anonymize the data using privacy preserving algorithms and then publish the data for the third party usage. In this paper the privacy threats associated with wearable devices is discussed. We also propose privacy preserving data publishing algorithm that is based on anatomy to preserve privacy on the data collected by wearables. The proposed algorithm takes 70% less time when compared to existing methods.