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.
Anonymity in CON. AND @math NA @cite_30 proposes a Tor-like anonymizing tool for CCN @cite_27 to provide provable anonymity. It also aims to privacy protection via simple tunneling. However, as discussed in , AND @math NA is an all-in-one'' solution that introduces latency and impedes caching. Whereas, fine-grained privacy solutions are needed, since a widespread use of tunneling would inherently take away most of CON benefits in terms of performance and scalability. To provide censorship resistance, @cite_57 describes an algorithm to mix legitimate sensitive content with so-called cover files'' to hide it. By monitoring the content, an adversary would only see the mixed'' content, which prevents him from censoring the content.
@cite_30 @cite_27 @cite_57
2962973644 2014952121 2091803449
Content-centric networking — also known as information-centric networking (ICN) — shifts emphasis from hosts and interfaces (as in today’s Internet) to data. Named data becomes addressable and routable, while locations that currently store that data become irrelevant to applications. Named Data Networking (NDN) is a large collaborative research effort that exemplifies the content-centric approach to networking. NDN has some innate privacyfriendly features, such as lack of source and destination addresses on packets. However, as discussed in this paper, NDN architecture prompts some privacy concerns mainly stemming from the semantic richness of names. We examine privacy-relevant characteristics of NDN and present an initial attempt to achieve communication privacy. Specifically, we design an NDN add-on tool, called ANDaNA, that borrows a number of features from Tor. As we demonstrate via experiments, it provides comparable anonymity with lower relative overhead. Network use has evolved to be dominated by content distribution and retrieval, while networking technology still speaks only of connections between hosts. Accessing content and services requires mapping from the what that users care about to the network's where. We present Content-Centric Networking (CCN) which treats content as a primitive - decoupling location from identity, security and access, and retrieving content by name. Using new approaches to routing named content, derived heavily from IP, we can simultaneously achieve scalability, security and performance. We implemented our architecture's basic features and demonstrate resilience and performance with secure file downloads and VoIP calls. The recent literature has hailed the benefits of content-oriented network architectures. However, such designs pose a threat to privacy by revealing a user's content requests. In this paper, we study how to ameliorate privacy in such designs. We present an approach that does not require any special infrastructure or shared secrets between the publishers and consumers of content. In lieu of any informational asymmetry, the approach leverages computational asymmetry by forcing the adversary to perform sizable computations to reconstruct each request. This approach does not provide ideal privacy, but makes it hard for an adversary to effectively monitor the content requests of a large number of users.