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.

翻译：智能医疗设备和大数据分析的普及显著推动了智慧医疗网络的发展。为提升诊断精度，智慧医疗网络中的不同参与者共享包含敏感信息的健康数据。因此，数据交换过程引发了隐私担忧，尤其是当多源健康数据的整合（链接攻击）导致进一步泄露时。链接攻击是隐私领域的一种主要攻击类型，可利用多种数据源进行隐私数据挖掘。此外，攻击者发起投毒攻击伪造健康数据，导致误诊甚至身体损伤。为保护隐私健康数据，我们提出了一种基于用户间信任等级的个性化差分隐私模型。信任度由定义的社区密度评估，而相应的隐私保护等级被映射为受差分隐私约束的可控随机噪声。为避免个性化差分隐私中的链接攻击，我们利用马尔可夫随机过程设计了噪声相关解耦机制。此外，我们在区块链上构建社区模型，可降低智慧医疗网络中差分隐私数据传输过程中投毒攻击的风险。为验证所提方法的有效性和优越性，我们在基准数据集上开展了广泛实验。