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Interactive Reputation Systems: How to Cope with Malicious Behavior in Feedback Mechanisms

机译:互动声誉系统:如何应对反馈机制中的恶意行为

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? 2017, The Author(s). ? 2017, The Author(s). Early reputation systems use simple computation metrics that can easily be manipulated by malicious actors. Advanced computation models that mitigate their weaknesses, however, are non-transparent to the end-users thus lowering their understandability and the users’ trust towards the reputation system. The paper proposes the concept of interactive reputation systems that combine the cognitive capabilities of the user with the advantages of robust metrics while preserving the system’s transparency. Results of the evaluation show that interactive reputation systems increase both the users’ detection ability (robustness) and understanding of malicious behavior while avoiding trade-offs in usability. Early reputation systems use simple computation metrics that can easily be manipulated by malicious actors. Advanced computation models that mitigate their weaknesses, however, are non-transparent to the end-users thus lowering their understandability and the users’ trust towards the reputation system. The paper proposes the concept of interactive reputation systems that combine the cognitive capabilities of the user with the advantages of robust metrics while preserving the system’s transparency. Results of the evaluation show that interactive reputation systems increase both the users’ detection ability (robustness) and understanding of malicious behavior while avoiding trade-offs in usability.
机译:还2017年,作者。还2017年,作者。早期声誉系统使用可通过恶意演员轻松操纵的简单计算度量。然而,高级计算模型减轻了他们的弱点对最终用户来说是对最终用户的透明度,从而降低他们对声誉系统的可理解性和用户的信任。本文提出了互动性声誉系统的概念,这些系统将用户的认知能力与强大的指标的优势相结合,同时保留了系统的透明度。评估结果表明,互动声誉系统增加了用户的检测能力(鲁棒性)和对恶意行为的理解,同时避免了可用性的权衡。早期声誉系统使用可通过恶意演员轻松操纵的简单计算度量。然而,高级计算模型减轻了他们的弱点对最终用户来说是对最终用户的透明度,从而降低他们对声誉系统的可理解性和用户的信任。本文提出了互动性声誉系统的概念,这些系统将用户的认知能力与强大的指标的优势相结合,同时保留了系统的透明度。评估结果表明,互动声誉系统增加了用户的检测能力(鲁棒性)和对恶意行为的理解,同时避免了可用性的权衡。

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