Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker can provide care teams with a trustworthy, transparent ecosystem that can facilitate information sharing and well-informed decisions for patient-centric care.

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In this paper, the proposed PCMRM (possibilistic based cross-media relevance model) annotates images based on their visual contents. PCMRM framework relies on unsupervised learning to group the visually similar image regions into homogeneous clusters, along with the cross-media relevance model (CMRM) that is used to estimate the joint distribution of textual keywords and images. Besides, the unsupervised learning task exploits the robustness to noise of a possibilistic clustering algorithm, and generates membership degrees that represent the typicality of image regions with respect to the obtained clusters. To validate and assess the proposed system, we used the standard Corel dataset. PCMRM produced promising results. The reported performance measures proved that the proposed automatic image annotation approach outperforms similar state of the art solutions. This attainment is mainly attributed to the exploitation of the possibilistic membership produced by the clustering algorithm which allowed accurate learning of the association between annotating labels and the visual content of the image regions.

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