Abstract
Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices.
Highlights
The release of contact tracing applications in response to the COVID-19 pandemic has highlighted some of the vulnerabilities and potential privacy issues that can be associated with these applications
The approach that is used for evaluation has heart rate (HR) samples, whilst other variables could be used, such as skin temperature, blood pressure, respiration rate, and indicators of specific diseases, such as diabetes, plethora signals, etc
Body temperature hardly varies or fluctuates in response to changes as the human body automatically maintains its value within a tight range, as shown in HR data were primarily measured and used in the experiment
Summary
The release of contact tracing applications in response to the COVID-19 pandemic has highlighted some of the vulnerabilities and potential privacy issues that can be associated with these applications. We propose an energy-efficient and secure data inference framework for IoHT applications, e.g., a smart house health care system (as demonstrated in Figure 1), which enables the collected sensitive information from the smart house health care system to be transferred in the encrypted domain while simultaneously reducing the energy consumption. The proposed framework involves two tiers, which consist of the data reduction tier and data protection tier This two-tier approach is designed for IoHT applications, where privacy in the underlying sensor data is protected by a privacy-preserving workflow. In view of the energy efficiency and privacy preservation concepts in this framework, a small number of beneficial applications have been examined, including patient monitoring during a pandemic, the battery conservation of personal health devices (PHD), and the use of biometrics for remote identification.
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