Achieving Content-Oriented Anonymity with CRISP
Achieving Content-Oriented Anonymity with CRISP
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DOI：
10.1109/tdsc.2015.2498603
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发表时间：
2017-11
影响因子：
7.3
通讯作者：
Peng Zhang;Qi Li;P. Lee
中科院分区：
文献类型：
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作者：
Peng Zhang;Qi Li;P. Lee
命名数据网络（NDN）作为信息中心网络（ICN）的一种流行实现，极大地提高了Internet内容分发的效率。NDN的一个特性是它提高了隐私性，因为内容消费者或发布者都不需要地址。然而，NDN数据包包含内容名称，因此一旦能够将数据包和用户联系起来，动机良好的攻击者仍然可以推断出用户正在请求什么内容。如何在NDN中提供隐私，鉴于其独特的数据检索模式，是一个开放的问题。在本文中，我们探讨了一个特定的面向内容的匿名模型称为内容用户不可链接性，打破了内容和请求用户之间的关系。我们认为，有效地实现内容用户的unlinkability是一个不平凡的任务，因为现有的隧道为基础的方法将在很大程度上解雇NDN的内容缓存，导致大的内容检索延迟。为此，我们提出了CRISP，即合作随机利益分配。在CRISP中，路由器合作形成全网状组，内容请求在转发给内容生产者之前在其中随机传播。我们表明CRISP可以实现可能的内容与用户的不可链接性与概率模型。大量的模拟表明，CRISP在内容检索延迟和数据吞吐量方面优于现有的解决方案，包括ANDANA和Crowds。
As a popular realization of Information-Centric Network (ICN), Named Data Networking (NDN) greatly improves the efficiency of Internet content-distribution. A feature of NDN is that it improves privacy, as no addresses are needed for either the content consumer or publisher. However, NDN packets contain content names, and hence a well-motivated adversary can still deduce what content the user is requesting once it can link the packets and users. How to provide privacy in NDN, given its unique data retrieval mode, is an open problem. In this paper, we explore a specific content-oriented anonymity model called content-user unlinkability, which breaks the relationship between the content and the requesting user. We argue that achieving content-user unlinkability efficiently is a non-trivial task, since existing tunnel-based approaches will largely dismiss content caching of NDN, resulting in large content retrieval delay. To this end, we propose CRISP, namely Cooperative Random IntereSt P ropagation. In CRISP, routers cooperate to form full-meshed groups, within which content requests are randomly propagated before they are forwarded to content producers. We show CRISP can achieve probable content-user unlinkability with probabilistic models. Extensive simulations demonstrate that CRISP outperforms existing solutions including ANDANA and Crowds, in terms of both content retrieval latency and data throughput.