As the Internet struggles to cope with scalability, mobility, and security issues, new network architectures are being proposed to better accommodate the needs of modern systems and applications. In particular, Content-Oriented Networking (CON) has emerged as a promising next-generation Internet architecture: it sets to decouple content from hosts, at the network layer, by naming data rather than hosts. CON comes with a potential for a wide range of benefits, including reduced congestion and improved delivery speed by means of content caching, simpler configuration of network devices, and security at the data level. However, it remains an interesting open question whether or not, and to what extent, this emerging networking paradigm bears new privacy challenges. In this paper, we provide a systematic privacy analysis of CON and the common building blocks among its various architectural instances in order to highlight emerging privacy threats, and analyze a few potential countermeasures. Finally, we present a comparison between CON and today's Internet in the context of a few privacy concepts, such as, anonymity, censoring, traceability, and confidentiality.
From the privacy perspective, work in @cite_48 @cite_37 identifies the issue of information leakage through caches in CCN. It proposes a few simple countermeasures, following and approaches. The former can be achieved using techniques similar to those addressing cache pollution attacks in IP @cite_53 , although such an approach can be difficult to port to CON due to the lack of source address. The latter can actually be global, i.e., treating all traffic as sensitive, delaying all traffic, or deploying a shared cache to circumvent the attack. Alternatively, a selective prevention approach may try to distinguish between sensitive and non-sensitive content, based on content popularity and context (time, location), and then delay or tunnel sensitive content. It is not clear, however, how to implement the selection mechanism to distinguish between private and non-private content, but authors of @cite_37 suggest to implement this service either in the network layer (i.e., the router classifies the content) or by the host (i.e., content source tags sensitive content). Such classification is in turn a very challenging task, since privacy is a relative notion that changes from one user to another. Also, are briefly discussed, although no countermeasures besides tunneling have been proposed.
@cite_48 @cite_37 @cite_53
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Named Data Networking architectures have been proposed to improve various shortcomings of the current Internet architecture. A key part of these proposals is the capability of caching arbitrary content in arbitrary network locations. While caching has the potential to improve network performance, the data stored in caches can be seen as transient traces of past communication that attackers can exploit to compromise the users' privacy. With this editorial note, we aim to raise awareness of privacy attacks as an intrinsic and relevant issue in Named Data Networking architectures. Countermeasures against privacy attacks are subject to a trade-off between performance and privacy. We discuss several approaches to countermeasures representing different incarnations of this tradeoff, along with open issues to be looked at by the research community. Proxy caching servers are widely deployed in today's Internet. While cooperation among proxy caches can significantly improve a network's resilience to denial-of-service (DoS) attacks, lack of cooperation can transform such servers into viable DoS targets. In this paper, we investigate a class of pollution attacks that aim to degrade a proxy's caching capabilities, either by ruining the cache file locality, or by inducing false file locality. Using simulations, we propose and evaluate the effects of pollution attacks both in Web and peer-to-peer (p2p) scenarios, and reveal dramatic variability in resilience to pollution among several cache replacement policies. We develop efficient methods to detect both false-locality and locality-disruption attacks, as well as a combination of the two. To achieve high scalability for a large number of clients requests without sacrificing the detection accuracy, we leverage streaming computation techniques, i.e., bloom filters and probabilistic counting. Evaluation results from large-scale simulations show that these mechanisms are effective and efficient in detecting and mitigating such attacks. Furthermore, a Squid-based implementation demonstrates that our protection mechanism forces the attacker to launch extremely large distributed attacks in order to succeed.