_:n70839b02516e4a018ac6e060ab783b6exb11
<http://schema.org/description>
"Privacy protection in electronic healthcare applications is an important\nconsideration due to the sensitive nature of personal health data. Internet of\nHealth Things (IoHT) networks have privacy requirements within a healthcare\nsetting. However, these networks have unique challenges and security\nrequirements (integrity, authentication, privacy and availability) must also be\nbalanced with the need to maintain efficiency in order to conserve battery\npower, which can be a significant limitation in IoHT devices and networks. Data\nare usually transferred without undergoing filtering or optimization, and this\ntraffic can overload sensors and cause rapid battery consumption when\ninteracting with IoHT networks. This consequently poses restrictions on the\npractical implementation of these devices. As a solution to address the issues,\nthis paper proposes a privacy-preserving two-tier data inference framework,\nthis can conserve battery consumption by reducing the data size required to\ntransmit through inferring the sensed data and can also protect the sensitive\ndata from leakage to adversaries. Results from experimental evaluations on\nprivacy show the validity of the proposed scheme as well as significant data\nsavings without compromising the accuracy of the data transmission, which\ncontributes to energy efficiency of IoHT sensor devices."
<https://cdnjs.deepai.org/profile/wencheng-yang>