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Secure Abstraction Views for Scientific Workflow Provenance Querying

机译：用于科学工作流出处查询的安全抽象视图

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摘要

Provenance has become increasingly important in scientific workflows and services computing to capture the derivation history of a data product, including the original data sources, intermediate data products, and the steps that were applied to produce the data product. In many cases, both scientific results and the used protocol are sensitive and effective access control mechanisms are essential to protect their confidentiality. In this paper, we propose: 1) a formal scientific workflow provenance model as the basis for querying and access control for workflow provenance; 2) a security model for fine-grained access control for multilevel provenance and an algorithm for the derivation of a full security specification based on inheritance, overriding, and conflict resolution; 3) a formalization of the notion of security views and an algorithm for security view derivation; and 4) a formalization of the notion of secure abstraction views and an algorithm for its computation. A prototype called SecProv has been developed, and experiments show the effectiveness and efficiency of our approach.
机译：在科学的工作流和服务计算中，来源已变得越来越重要，以捕获数据产品的派生历史，包括原始数据源，中间数据产品以及应用于生产数据产品的步骤。在许多情况下，科学结果和使用的协议都是敏感的，有效的访问控制机制对于保护其机密性至关重要。在本文中，我们提出：1）正式的科学工作流出处模型作为工作流出处查询和访问控制的基础； 2）用于多级来源的细粒度访问控制的安全模型，以及用于基于继承，覆盖和冲突解决方案导出完整安全规范的算法； 3）安全视图概念的形式化和安全视图派生算法；和4）安全抽象视图概念的形式化及其计算算法。已经开发出一个名为SecProv的原型，并且实验证明了我们方法的有效性和效率。

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