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首页> 外文期刊>International journal of grid and high performance computing >Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks
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Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks

机译：隐私增强的基于云的推荐服务，用于在医疗保健社交网络中隐式发现相关支持组

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摘要

Recommending support-groups in healthcare social networks is the problem of detecting for each patient his/her membership to one support-group of relevant patients. The patients in each support-group share some relevant preferences which guarantee that the support-group as a whole satisfies some desired properties of similarity. As a result, forming these support-groups requires the availability of personal data of different patients. This is a crucial requirement for different recommender services. With the increasing trend of service providers to collect a large volume of personal data regarding their end-users, presumably to better serve them. However, a significant part of the data that is typically collected is not essential to the service being offered, or to the completion of the services it was presumably released for. Gathering such unnecessary data can be seen as a privacy threat, and storing it exposes the end-users to further unavoidable risks. In this paper, a privacy enhanced cloud-based recommendation service is proposed for the implicit discovery of appropriate support groups in healthcare social network. A fog based middleware (FMCP) was introduced that runs at patients' sides and allows exchanging of their information to facilities recommending and creating support-groups without disclosing their real preferences to other parties. The membership of patients in various support groups allows receiving highly appropriate and reliable healthcare-related advices. The system utilizes two protocols to attain this goal. Experiments were performed on real dataset.
机译：在医疗保健社交网络中推荐支持小组是一个问题，即为每个患者检测其所属的相关患者支持小组的成员资格。每个支持组中的患者都有一些相关的偏好，这些偏好保证了支持组整体上满足某些所需的相似性。结果，形成这些支持小组需要获得不同患者的个人数据。这是不同推荐服务的关键要求。随着服务提供商越来越多地收集有关其最终用户的大量个人数据，以更好地为他们提供服务。但是，通常收集的数据中的很大一部分对于所提供的服务或完成其大概为之发布的服务并不是必需的。收集此类不必要的数据可被视为隐私威胁，存储这些数据会使最终用户面临不可避免的风险。在本文中，提出了一种基于隐私增强的基于云的推荐服务，用于隐式发现医疗保健社交网络中的适当支持组。引入了一种基于雾的中间件（FMCP），该中间件在患者侧运行，并允许将他们的信息交换给推荐和创建支持小组的设施，而无需向其他方透露他们的实际偏好。各个支持小组中患者的成员资格允许接受高度适当且可靠的医疗保健相关建议。该系统利用两种协议来实现这一目标。实验是在真实数据集上进行的。

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