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Environmental context aware trust in mobile P2P networks

机译:对移动P2P网络的环境感知感知信任

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With the growing popularity and capabilities of mobile devices, peer-to-peer networking among such devices is increasingly of interest for mobile content sharing. One of the major challenges in practical use of Mobile Peer-to-Peer networks (MP2P) is the trust among peers. Traditionally, solutions in the state of the art have focused on a peer's past experience in evaluating trust of other peers, based on direct interactions. Previously unknown peers (with no history of direct interactions) are assessed based on third party recommendations, yet again requiring a peer to evaluate and find trustworthy recommenders. This reveals the fundamental need to find peers with honest intentions before any interaction. It becomes challenging when no known peers are in the vicinity, which is highly likely in an MP2P scenario. For a general mobile user, the probability of encountering trustworthy peers in particular situations or environmental contexts may be higher than in other contexts, e.g. in office than on the road while traveling. Further, observed peers which are co-located over a number of environmental contexts may have more in common and thus resulting a higher mutual trust. These facts can be utilized to enrich the trust derivation process in a decentralized manner. In this paper, we propose a generalized and a novel distributed mechanism to estimate the trust for peers using their encounter history in different environmental contexts, and a way to prioritize contexts depending on the level of association with them. When evaluated against real user data of the reality mining dataset, the results of the proposed mechanism show a significantly improved accuracy of trust evaluation compared to the state of the art.
机译:随着移动设备的普及和功能的日益增长,此类设备之间的对等网络越来越受到移动内容共享的关注。实际使用移动对等网络(MP2P)的主要挑战之一是对等方之间的信任。传统上,现有技术的解决方案集中在对等方过去的经验上,这些经验是基于直接交互来评估其他对等方的信任度的。以前未知的同伴(没有直接交互的历史记录)是根据第三方推荐进行评估的,但再次要求同伴评估并找到可信赖的推荐者。这揭示了在进行任何交互之前找到具有诚实意图的同伴的基本需求。当附近没有已知的对等方时,这将变得具有挑战性,这在MP2P场景中极有可能发生。对于一般的移动用户,在特定情况下或环境情况下遇到可信赖的对等方的概率可能比在其他情况下更高,例如,在办公室里,而不是在旅途中。此外,在多个环境上下文中共处一处的观察到的对等点可能具有更多的共同点,从而导致更高的相互信任度。这些事实可用于以分散的方式丰富信任推导过程。在本文中,我们提出了一种通用的,新颖的分布式机制来使用对等体在不同环境上下文中的遭遇历史来估计对等体的信任度,以及一种根据与它们之间的关联级别对上下文进行优先级排序的方法。当根据现实挖掘数据集的真实用户数据进行评估时,与现有技术相比,所提出机制的结果显示出信任评估的准确性显着提高。

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