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arXiv 1603.03501cs.NIcs.CR
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AccConF:一种在ICN中利用网内缓存数据的访问控制框架

AccConF: An Access Control Framework for Leveraging In-Network Cached Data in ICNs

S. Misra, R. Tourani, F. Natividad, T. Mick, N. Majd, H. Huang

摘要:

快速增长的互联网流量越来越多地以内容为基础,并由移动用户驱动,用户对数据而不是其来源更感兴趣。这就迫切需要一个以信息为中心的互联网架构。对信息中心网络(ICN)的研究已经产生了新颖的架构,例如,CCN/NDN、DONA和PSIRP/PURSUIT;都同意将基于命名数据的寻址和普遍缓存作为整体设计组件。通过网络范围的内容缓存,内容访问控制策略的实施变得不平凡。网络中的每个缓存节点都需要在内容提供方的帮助下实施访问控制策略。这变得效率低下,并且容易出现无限延迟,特别是在提供商中断期间。在本文中,我们提出了一个有效的访问控制框架ICN,它允许合法用户直接访问和使用缓存的内容,并不需要验证/认证的在线提供商认证服务器或内容服务路由器。这个框架将有助于减少服务器中断造成的系统停机时间的影响,并通过利用缓存来减少交付延迟,同时保证只对合法用户进行访问。实验/仿真结果表明,该方案的适用性,所有用户,但特别是移动用户,特别是在安全性和延迟开销方面。

英文摘要:

The fast-growing Internet traffic is increasingly becoming content-based and driven by mobile users, with users more interested in data rather than its source. This has precipitated the need for an information-centric Internet architecture. Research in information-centric networks (ICNs) have resulted in novel architectures, e.g., CCN/NDN, DONA, and PSIRP/PURSUIT; all agree on named data based addressing and pervasive caching as integral design components. With network-wide content caching, enforcement of content access control policies become non-trivial. Each caching node in the network needs to enforce access control policies with the help of the content provider. This becomes inefficient and prone to unbounded latencies especially during provider outages. In this paper, we propose an efficient access control framework for ICN, which allows legitimate users to access and use the cached content directly, and does not require verification/authentication by an online provider authentication server or the content serving router. This framework would help reduce the impact of system down-time from server outages and reduce delivery latency by leveraging caching while guaranteeing access only to legitimate users. Experimental/simulation results demonstrate the suitability of this scheme for all users, but particularly for mobile users, especially in terms of the security and latency overheads.

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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