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A roadmap for privacy-enhanced secure data provenance

机译:增强隐私的安全数据来源的路线图

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The notion of data provenance was formally introduced a decade ago and has since been investigated, but mainly from a functional perspective, which follows the historical pattern of introducing new technologies with the expectation that security and privacy can be added later. Despite very recent interests from the cyber security community on some specific aspects of data provenance, there is no long-haul, overarching, systematic framework for the security and privacy of provenance. The importance of secure provenance R&D has been emphasized in the recent report on Federal game-changing R&D for cyber security especially with respect to the theme of Tailored Trustworthy Spaces. Secure data provenance can significantly enhance data trustworthiness, which is crucial to various decision-making processes. Moreover, data provenance can facilitate accountability and compliance (including compliance with privacy preferences and policies of relevant users), can be an important factor in access control and usage control decisions, and can be valuable in data forensics. Along with these potential benefits, data provenance also poses a number of security and privacy challenges. For example, sometimes provenance needs to be confidential so it is visible only to properly authorized users, and we also need to protect the identity of entities in the provenance from exposure. We thus need to achieve high assurance of provenance without comprising privacy of those in the chain that produced the data. Moreover, if we expect voluntary large-scale participation in provenance-aware applications, we must assure that the privacy of the individuals or organizations involved will be maintained. It is incumbent on the cyber security community to develop a technical and scientific framework to address the security and privacy challenges so that our society can gain maximum benefit from this technology. In this paper, we discuss a framework of theoretical foundations, models, mechanisms and architectures that allow applications to benefit from privacy-enhanced and secure use of provenance in a modular fashion. After introducing the main components of such a framework and the notion of provenance life cycle, we discuss in details research questions and issues concerning each such component and related approaches.
机译:数据来源的概念是在十年前正式引入的,此后已经进行了研究,但主要是从功能角度出发,它遵循引入新技术的历史模式,并期望可以在以后添加安全性和隐私性。尽管网络安全社区最近对数据出处的某些特定方面产生了兴趣,但对于出处的安全性和私密性,还没有长期,全面的系统框架。在最近关于联邦政府改变网络安全的改变游戏规则的研发的报告中,特别是在量身定制的可信赖空间这一主题上,强调了安全出处研发的重要性。安全的数据来源可以显着提高数据的可信赖性,这对于各种决策过程至关重要。此外,数据出处可促进问责制和合规性(包括遵守隐私权偏好和相关用户的政策),可能是访问控制和使用控制决策中的重要因素,并且在数据取证中很有价值。除了这些潜在的好处之外,数据源还带来了许多安全和隐私挑战。例如,有时出处需要保密,因此只有经过适当授权的用户才能看到它,并且我们还需要保护出处中实体的身份以防暴露。因此,我们需要实现高度的来源保证,而又不对产生数据的链中的人员保密。此外,如果我们希望自愿大规模参与起源识别应用程序,则必须确保所涉及的个人或组织的隐私得到维护。网络安全社区有责任开发一种技术和科学框架来应对安全和隐私挑战,以便我们的社会能够从该技术中获得最大的收益。在本文中,我们讨论了一个理论基础,模型,机制和体系结构的框架,这些框架使应用程序能够以模块化的方式受益于隐私增强和安全地使用来源。在介绍了这种框架的主要组成部分以及出处生命周期的概念之后,我们将详细讨论与每个此类组成部分和相关方法有关的研究问题和问题。

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