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The popularization of intelligent healthcare devices and big data analyticssignificantly boosts the development of smart healthcare networks (SHNs). Toenhance the precision of diagnosis, different participants in SHNs share healthdata that contains sensitive information. Therefore, the data exchange processraises privacy concerns, especially when the integration of health data frommultiple sources (linkage attack) results in further leakage. Linkage attack isa type of dominant attack in the privacy domain, which can leverage variousdata sources for private data mining. Furthermore, adversaries launch poisoningattacks to falsify the health data, which leads to misdiagnosing or evenphysical damage. To protect private health data, we propose a personalizeddifferential privacy model based on the trust levels among users. The trust isevaluated by a defined community density, while the corresponding privacyprotection level is mapped to controllable randomized noise constrained bydifferential privacy. To avoid linkage attacks in personalized differentialprivacy, we designed a noise correlation decoupling mechanism using a Markovstochastic process. In addition, we build the community model on a blockchain,which can mitigate the risk of poisoning attacks during differentially privatedata transmission over SHNs. To testify the effectiveness and superiority ofthe proposed approach, we conduct extensive experiments on benchmark datasets.

Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective