电信网络中智能垃圾邮件发送者的分散隐私感知协同过滤

M. A. Azad, Samiran Bag
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引用次数: 13

摘要

聪明的垃圾邮件发送者和电话营销人员通过向分布在许多电信运营商中的大量收件人进行低费率的垃圾邮件活动来绕过独立的垃圾邮件检测系统。多家电信运营商之间的合作将允许运营商在垃圾邮件活动的早期阶段摆脱不受欢迎的呼叫者。设计协作式垃圾邮件检测系统的挑战是,由于隐私问题,OPs不愿意分享有关其用户/客户行为的某些信息。理想情况下,如果合作过程能够确保用户及其网络数据的完全隐私保护,运营商同意共享某些汇总的统计信息。为了解决这一挑战并说服OPs进行协作,本文提出了一种分散的声誉聚合协议,该协议使OPs能够在不使用受信任的第三方集中式系统的情况下参与协作过程,并且无需与其他OPs建立预定义的信任关系。在这种程度上,运营商之间的协作是通过OPs之间的加密信誉分数交换来实现的,即使存在串谋者,也能充分保护用户的关系网络和信誉分数。我们在由五个合作者组成的模拟数据上评估了所提出协议的性能。实验结果表明,该方法在真阳性率和假阳性率方面优于独立系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decentralized privacy-aware collaborative filtering of smart spammers in a telecommunication network
Smart spammers and telemarketers circumvent the standalone spam detection systems by making low rate spam-ming activity to a large number of recipients distributed across many telecommunication operators. The collaboration among multiple telecommunication operators (OPs) will allow operators to get rid of unwanted callers at the early stage of their spamming activity. The challenge in the design of collaborative spam detection system is that OPs are not willing to share certain information about behaviour of their users/customers because of privacy concerns. Ideally, operators agree to share certain aggregated statistical information if collaboration process ensures complete privacy protection of users and their network data. To address this challenge and convince OPs for the collaboration, this paper proposes a decentralized reputation aggregation protocol that enables OPs to take part in a collaboration process without use of a trusted third party centralized system and without developing a predefined trust relationship with other OPs. To this extent, the collaboration among operators is achieved through the exchange of cryptographic reputation scores among OPs thus fully protects relationship network and reputation scores of users even in the presence of colluders. We evaluate the performance of proposed protocol over the simulated data consisting of five collaborators. Experimental results revealed that proposed approach outperforms standalone systems in terms of true positive rate and false positive rate.
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