TRIBE: Trust revision for information based on evidence

M. Sensoy, Geeth de Mel, Lance M. Kaplan, T. Pham, T. Norman
{"title":"TRIBE: Trust revision for information based on evidence","authors":"M. Sensoy, Geeth de Mel, Lance M. Kaplan, T. Pham, T. Norman","doi":"10.5072/ZENODO.21406","DOIUrl":null,"url":null,"abstract":"In recent years, the number of information sources available to support decision-making has increased dramatically. However, more information sources do not always mean higher precision in the fused information. This is partially due to the fact that some of these sources may be erroneous or malicious. Therefore, it is critical to asses the trust in information before performing fusion. To estimate trust in information, existing approaches use trustworthiness of its source as a proxy. We argue that conflicts between information may also serve as evidence to reduce trust in information. In this paper, we use subjective opinions to represent information from diverse sources. We propose to exploit conflicts between opinions to revise their trustworthiness. For this purpose, we formalise trust revision as a constraint optimisation problem. Through extensive empirical studies, we show that our approach significantly outperform existing ones in the face of malicious information sources.","PeriodicalId":117803,"journal":{"name":"Proceedings of the 16th International Conference on Information Fusion","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th International Conference on Information Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5072/ZENODO.21406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

In recent years, the number of information sources available to support decision-making has increased dramatically. However, more information sources do not always mean higher precision in the fused information. This is partially due to the fact that some of these sources may be erroneous or malicious. Therefore, it is critical to asses the trust in information before performing fusion. To estimate trust in information, existing approaches use trustworthiness of its source as a proxy. We argue that conflicts between information may also serve as evidence to reduce trust in information. In this paper, we use subjective opinions to represent information from diverse sources. We propose to exploit conflicts between opinions to revise their trustworthiness. For this purpose, we formalise trust revision as a constraint optimisation problem. Through extensive empirical studies, we show that our approach significantly outperform existing ones in the face of malicious information sources.
TRIBE:基于证据的信息信任修正
近年来,可用于支持决策的信息来源的数量急剧增加。然而,信息源越多并不意味着融合信息的精度越高。这部分是由于这些来源中的一些可能是错误的或恶意的。因此,在进行融合之前,对信息的信任进行评估是至关重要的。为了估计信息的可信度,现有的方法使用信息来源的可信度作为代理。我们认为,信息之间的冲突也可能作为证据,以减少对信息的信任。在本文中,我们使用主观意见来表示来自不同来源的信息。我们建议利用意见之间的冲突来修正其可信度。为此,我们将信任修正形式化为约束优化问题。通过广泛的实证研究,我们表明我们的方法在面对恶意信息源时明显优于现有的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信