Toward Privacy-Preserving Healthcare Monitoring Based on Time-Series Activities Over Cloud
Yandong Zheng, Rongxing Lu, Songnian Zhang, Yunguo Guan, Jun Shao, Hui Zhu · IEEE Internet of Things Journal · 2021
The thriving of the Internet of Things (IoT) has become the enabler of smart eHealthcare, which greatly benefits patients by providing various data-driven healthcare monitoring services. Among those promising services, the time-series activities-based healthcare monitoring service is highly regarded due to its popularity. Meanwhile, with the rapidly growing volume of healthcare data, an emerging trend is to outsource the time-series activities-based healthcare monitoring models and the corresponding services to a cloud, which, however, inevitably entails privacy concerns. Although many existing works have put forth some solutions for privacy-preserving time-series activities-based healthcare monitoring, they are not applicable to the outsourced scenario with a single-server setting. To address the challenge, in this article, we propose an efficient and privacy-preserving forward algorithm (PPFA) and further apply PPFA to construct a remote healthcare monitoring scheme over the cloud. To the best of our knowledge, our PPFA is the first privacy-preserving forward algorithm over cloud while without any accuracy loss. In addition, our remote healthcare monitoring scheme is also the first privacy-preserving hidden Markov model-based healthcare monitoring scheme in the single-server setting. Detailed security analysis shows that our PPFA and healthcare monitoring scheme are indeed privacy preserving. In addition, extensive simulations are conducted, and the results also demonstrate their efficiencies.