临床决策支持系统的不可否认来源
Non-repudiable provenance for clinical decision support systems
摘要：
来源模板现在是构建数据来源记录的公认方法。每个模板以抽象形式定义特定于域的动作的出处，然后可以根据对出处模板服务的单个调用的需要来实例化该动作。随着数据可靠性和可信度在越来越多的领域中成为关键问题，相应地需要确保数据的来源是不可否认的。在本文中，我们贡献了两个新的，互补的模块，我们的模板模型和实现产生不可否认的数据出处。第一个模块，跟踪起源模板服务本身的操作，并在各个服务调用的级别上记录对象级别文档的构造的起源跟踪。第二个是不可否认模块，它为每个调用记录的数据生成证据，相应地注释服务跟踪，并将该证据的表示提交给提供者不可知的公证服务。我们评估我们的方法在临床决策支持系统的背景下的适用性。我们首先定义一个策略，以确保安全威胁分析的证据的不可否认性，以证明我们的解决方案的适用性。然后，我们从一个特定的系统中选择三个用例，咨询，对比数据出处记录要求，并分析我们的原型实现对三个不同的公证提供商的后续性能。
英文摘要：
Provenance templates are now a recognised methodology for the construction of data provenance records. Each template defines the provenance of a domain-specific action in abstract form, which may then be instantiated as required by a single call to the provenance template service. As data reliability and trustworthiness becomes a critical issue in an increasing number of domains, there is a corresponding need to ensure that the provenance of that data is non-repudiable. In this paper we contribute two new, complementary modules to our template model and implementation to produce non-repudiable data provenance. The first, a module that traces the operation of the provenance template service itself, and records a provenance trace of the construction of an object-level document, at the level of individual service calls. The second, a non-repudiation module that generates evidence for the data recorded about each call, annotates the service trace accordingly, and submits a representation of that evidence to a provider-agnostic notary service. We evaluate the applicability of our approach in the context of a clinical decision support system. We first define a policy to ensure the non-repudiation of evidence with respect to a security threat analysis in order to demonstrate the suitability of our solution. We then select three use cases from within a particular system, Consult, with contrasting data provenance recording requirements and analyse the subsequent performance of our prototype implementation against three different notary providers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
