  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 have privacy requirements within a healthcare
setting. However, these networks have unique challenges and security
requirements (integrity, authentication, privacy and availability) 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 consequently poses restrictions on the
practical implementation of these devices. As a solution to address the issues,
this paper proposes a privacy-preserving two-tier data inference framework,
this can conserve battery consumption by reducing the data size required to
transmit through inferring the sensed data and can also protect the sensitive
data from leakage to adversaries. Results from experimental evaluations on
privacy show the validity of the proposed scheme as well as significant data
savings without compromising the accuracy of the data transmission, which
contributes to energy efficiency of IoHT sensor devices.
