In contrast to today's IP-based host-oriented Internet architecture, Information-Centric Networking (ICN) emphasizes content by making it directly addressable and routable. Named Data Networking (NDN) architecture is an instance of ICN that is being developed as a candidate next-generation Internet architecture. By opportunistically caching content within the network, NDN appears to be well-suited for large-scale content distribution and for meeting the needs of increasingly mobile and bandwidth-hungry applications that dominate today's Internet. One key feature of NDN is the requirement for each content object to be digitally signed by its producer. Thus, NDN should be, in principle, immune to distributing fake (aka "poisoned") content. However, in practice, this poses two challenges for detecting fake content in NDN routers: (1) overhead due to signature verification and certificate chain traversal, and (2) lack of trust context, i.e., determining which public keys are trusted to verify which content. Because of these issues, NDN does not force routers to verify content signatures, which makes the architecture susceptible to content poisoning attacks. This paper explores root causes of, and some cures for, content poisoning attacks in NDN. In the process, it becomes apparent that meaningful mitigation of content poisoning is contingent upon a network-layer trust management architecture, elements of which we construct, while carefully justifying specific design choices. This work represents the initial effort towards comprehensive trust management for NDN.
@cite_36 proposes a framework for calculating a network entity's reputation score based on previous interactions feedback. In this framework, each service can apply its own reputation scoring functions. It also supports caching of trust evaluation to reduce network overhead, and provides an API for reporting feedback and calculating reputation scores.
@cite_36
2105040129 2164146906 2097726984 2007842529
Many reputation management systems have been developed under the assumption that each entity in the system will use a variant of the same scoring function. Much of the previous work in reputation management has focused on providing robustness and improving performance for a given reputation scheme. In this paper, we present a reputation-based trust management framework that supports the synthesis of trust-related feedback from many different entities while also providing each entity with the flexibility to apply different scoring functions over the same feedback data for customized trust evaluations. We also propose a novel scheme to cache trust values based on recent client activity. To evaluate our approach, we implemented our trust management service and tested it on a realistic application scenario in both LAN and WAN distributed environments. Our results indicate that our trust management service can effectively support multiple scoring functions with low overhead and high availability. Reputation systems rate the contributions to participatory sensing campaigns from each user by associating a reputation score. The reputation scores are used to weed out incorrect sensor readings. However, an adversary can deanonmyize the users even when they use pseudonyms by linking the reputation scores associated with multiple contributions. Since the contributed readings are usually annotated with spatiotemporal information, this poses a serious breach of privacy for the users. In this paper, we address this privacy threat by proposing a framework called IncogniSense. Our system utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. The reputation transfer process has an inherent trade-off between anonymity protection and loss in reputation. We investigate by means of extensive simulations several reputation cloaking schemes that address this tradeoff in different ways. Our system is robust against reputation corruption and a prototype implementation demonstrates that the associated overheads are minimal. Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems. In this paper, we propose a user reputation model and apply it to a user-interactive question answering system. It combines the social network analysis approach and the user rating approach. Social network analysis is applied to analyze the impact of participant users' relations to their reputations. User rating is used to acquire direct judgment of a user's reputation based on other users' experiences with this user. Preliminary experiments show that the computed reputations based on our proposed reputation model can reflect the actual reputations of the simulated roles and therefore can fit in well with our user-interactive question answering system. Copyright © 2006 John Wiley & Sons, Ltd.