The popularity of wearable devices equipped with a variety of sensors that can measure users' health status and monitor their lifestyle has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data from users. Although the health data collected via wearable devices are useful for providing healthcare services, the indiscriminate collection of an individual's health data raises serious privacy concerns. This is because the health data measured and monitored by wearable devices contain sensitive information related to the wearer's personal health and lifestyle. Therefore, we propose a method to aggregate health data obtained from users' wearable devices in a privacy-preserving manner. The proposed method leverages local differential privacy, which is a de facto standard for privacy-preserving data processing and aggregation, to collect sensitive health data. In particular, to mitigate the error incurred by the perturbation mechanism of location differential privacy, the proposed scheme first samples a small number of salient data that best represents the original health data, after which the scheme collects the sampled salient data instead of the entire set of health data. Our experimental results show that the proposed sampling-based collection scheme achieves significant improvement in the estimated accuracy when compared with straightforward solutions. Furthermore, the experimental results verify that an effective tradeoff between the level of privacy protection and the accuracy of aggregate statistics can be achieved with the proposed approach.
Bibliographical noteFunding Information:
Funding: This research was supported by the Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00269, A research on safe and convenient big data processing methods) and the Basic Science Research Program through the National Research Foundation of Korea (NRF-2020R1F1A1072622).
© 2020 by the authors.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes