Enhancing Collusion Resilience in Reputation Systems

Shen, Haiying; Lin, Yu-Hua; Sapra, Karan; Li, Ze

doi:10.1109/tpds.2015.2489198

Cited by 17 publications

(8 citation statements)

References 25 publications

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“…In platforms that employ such reputation systems, network metrics can highlight collusive behaviour. For example, Shen et al present algorithms for collusion detection, which can minimise the risk of reputation misuse [35].…”

Section: Social Network Analysismentioning

confidence: 99%

“…Collusive behaviour is a prevalent issue in systems where reputation can be accrued [1,35]. In general, such behaviour is characterised by two or more users cooperatively boosting their reputation by providing multiple high ratings to each other, while ignoring or giving low ratings to other users.…”

Section: Potential Collusive Behaviourmentioning

confidence: 99%

“…We use a simplified implementation of the algorithm proposed in [35] to determine whether a significant proportion of a user's total reputation comes from another node, and whether that node reciprocates. If this is the case, then these two nodes are flagged as potential colluders.…”

Section: Potential Collusive Behaviourmentioning

confidence: 99%

“…(2) Manipulative activity. It is also to be expected, given the potential benefits of having a high reputation on commonfare.net, that users may try to increase their perceived contribution by creating meaningless content, or colluding with other users by repeatedly engaging in meaningless interactions [1,35]. Ensuring these users are not rewarded for such actions is necessary to keep commonfare.net fair and equal.…”

Section: Network Analysis Viewmentioning

confidence: 99%

See 3 more Smart Citations

Supporting Social Innovation through Visualisations of Community Interactions

Rough

Paoli

Botto

2019

Proceedings of the 9th International Conference on Communities &Amp; Technologies - Transforming Communities

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Online communities that form through the introduction of sociotechnical platforms require significant effort to cultivate and sustain. Providing open, transparent information on community behaviour can motivate participation from community members themselves, while also providing platform administrators with detailed interaction dynamics. However, challenges arise in both understanding what information is conducive to engagement and sustainability, and then how best to represent this information to platform stakeholders. Towards a better understanding of these challenges, we present the design, implementation, and evaluation of a set of simple visualisations integrated into a Collective Awareness Platform for Social Innovation platform titled commonfare.net. We discuss the promise and challenge of bringing social innovation into the digital age, in terms of supporting sustained platform use and collective action, and how the introduction of community visualisations has been directed towards achieving this goal.

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Section: Social Network Analysismentioning

confidence: 99%

Section: Potential Collusive Behaviourmentioning

confidence: 99%

Section: Potential Collusive Behaviourmentioning

confidence: 99%

Section: Network Analysis Viewmentioning

confidence: 99%

See 2 more Smart Citations

Supporting Social Innovation through Visualisations of Community Interactions

Rough

Paoli

Botto

2019

Proceedings of the 9th International Conference on Communities &Amp; Technologies - Transforming Communities

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“…We can see that a disturbance 4 The setting of these cheating intensities should be reasonable considering the well accepted fact that the MCS is a relatively good community with a limited ratio of malicious behaviors (e.g. 4% in [17], or 10% in [18]). are correspondingly downgraded for at least 1.64%, 3.44% and 2.20%, respectively.…”

Section: B Impact Of General Cheating Intensitymentioning

confidence: 99%

Cheating-resilient incentive scheme for mobile crowdsensing systems

Zhao

Yang

et al. 2017

2017 14th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Abstract-MobileCrowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their selfinterest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive realworld trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher.

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