| Résumé | The convergence of Internet of Things (IoT) and smart healthcare technologies has opened up various promising applications that can significantly improve the quality of healthcare services. Among those applications, predicting patients' physical health based on their routine activities data collected from IoT devices is one of the most popular applications, where patients' data are considered as time-series activities and patients' physical health can be predicted by a classification model. Though many existing works have been exploited in this application, they either impose the computational costs of the classification on the healthcare center (e.g., hospitals) or delegate the cloud to process the classification without considering the privacy issues. However, since the healthcare center may not be powerful in computing and the cloud is not fully trusted, there is a high demand in offloading the computational cost of the healthcare center to the cloud while preserving the privacy of classification result against the cloud. Aiming at this challenge, in this paper, we present a novel privacy-preserving time-series activities classification algorithm by using hidden Markov model (HMM). Specifically, we first design a variant of forward algorithm of HMM and further introduce a privacy-preserving variant of forward (PPVF) protocol for the variant of forward algorithm. Then, based on the PPVF protocol, we propose our classification algorithm, which can offload the computational cost of the healthcare center to the cloud and preserve the privacy of classification result. Finally, security analysis and performance show that our proposal is not only privacy-preserving but also efficient in terms of lower computational cost. |
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