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arXiv 1310.6299cs.PL
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起源的核心演算

A Core Calculus for Provenance

Umut A. Acar, Amal Ahmed, James Cheney, Roly Perera

摘要:

由于在网络和其他计算机系统上共享和处理科学数据的持续革命,来源越来越受到关注。建议许多计算机系统将需要变得知晓出处,以便为科学数据或其他高价值数据提供令人满意的责任、再现性和信任。到目前为止,还没有一个共识,适当的正式模型或安全属性的出处。在以前的工作中,我们介绍了一个正式的起源安全框架,并提出了正式定义的属性称为披露和混淆。在这篇文章中,我们研究了在一个具体的环境中,即在一个通用的编程语言中,积极和消极的披露和混淆的精炼概念。以前的起源模型集中在特殊用途的语言,如工作流和数据库查询。我们考虑一个高阶的,函数式语言的总和,产品,递归类型和功能,并配备了跟踪语义,跟踪本身可以作为计算重放。我们提出了一个注释传播框架,支持许多出处的痕迹,包括标准形式的出处研究。我们研究了来源视图之间的一些关系,并开发了一些部分的解决方案的披露和混淆的问题,包括正确的算法披露和积极的混淆的基础上跟踪切片。

英文摘要:

Provenance is an increasing concern due to the ongoing revolution in sharing and processing scientific data on the Web and in other computer systems. It is proposed that many computer systems will need to become provenance-aware in order to provide satisfactory accountability, reproducibility, and trust for scientific or other high-value data. To date, there is not a consensus concerning appropriate formal models or security properties for provenance. In previous work, we introduced a formal framework for provenance security and proposed formal definitions of properties called disclosure and obfuscation. In this article, we study refined notions of positive and negative disclosure and obfuscation in a concrete setting, that of a general-purpose programing language. Previous models of provenance have focused on special-purpose languages such as workflows and database queries. We consider a higher-order, functional language with sums, products, and recursive types and functions, and equip it with a tracing semantics in which traces themselves can be replayed as computations. We present an annotation-propagation framework that supports many provenance views over traces, including standard forms of provenance studied previously. We investigate some relationships among provenance views and develop some partial solutions to the disclosure and obfuscation problems, including correct algorithms for disclosure and positive obfuscation based on trace slicing.

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。

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