  The popularization of intelligent healthcare devices and big data analytics
significantly boosts the development of smart healthcare networks (SHNs). To
enhance the precision of diagnosis, different participants in SHNs share health
data that contains sensitive information. Therefore, the data exchange process
raises privacy concerns, especially when the integration of health data from
multiple sources (linkage attack) results in further leakage. Linkage attack is
a type of dominant attack in the privacy domain, which can leverage various
data sources for private data mining. Furthermore, adversaries launch poisoning
attacks to falsify the health data, which leads to misdiagnosing or even
physical damage. To protect private health data, we propose a personalized
differential privacy model based on the trust levels among users. The trust is
evaluated by a defined community density, while the corresponding privacy
protection level is mapped to controllable randomized noise constrained by
differential privacy. To avoid linkage attacks in personalized differential
privacy, we designed a noise correlation decoupling mechanism using a Markov
stochastic process. In addition, we build the community model on a blockchain,
which can mitigate the risk of poisoning attacks during differentially private
data transmission over SHNs. To testify the effectiveness and superiority of
the proposed approach, we conduct extensive experiments on benchmark datasets.
