ITR: Computational Principles of Trust

ITR：信任计算原理

• 批准号: 0081742
• 负责人: Munindar Singh
• 金额: $ 47.66万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0081742&HistoricalAwards=false
• 关键词: ITR; Computational; Principles; Trust

The worldwide expansion of network access is driving an increase in interactions among people, among businesses, and between people and businesses. Such interactions are the staple of major Internet uses such as electronic commerce and virtual communities. Successful interaction relies heavily upon trust. Whereas security seeks to prevent illegal actions, trust goes beyond security in seeking assurance of accountability (of intent and capability) even for legal actions. However, figuring out whom to trust and to what extent is extremely difficult in an open networked environment such as the Internet. This project will study distributed, scalable computational approaches for trust management taking into account the different forms in which trust is exhibited in networked environments. Such approaches rely less on centralized authorities and more on community policing through reputation mechanisms. This project will evaluate the approaches based on criteria such as how easily a given approach can be bootstrapped, how efficient it is in helping members find competent and good peers, and how immune it is to invasion by untrustworthy members. This project will also study aggregate phenomena such as the emergence of subcommunities, linkages among subcommunities, and the sensitivity of a community to change in membership.

网络接入在全球范围内的扩展正在推动人与人之间、企业之间以及人与企业之间的互动增加。 这种互动是电子商务和虚拟社区等主要互联网用途的主要内容。 成功的互动在很大程度上依赖于信任。 安全旨在防止非法行动，而信任则超越安全，寻求对（意图和能力的）问责制的保证，即使是对法律的行动。 然而，在开放的网络环境（如Internet）中，弄清楚信任谁以及信任到何种程度是极其困难的。 这个项目将研究分布式的，可扩展的计算方法的信任管理考虑到不同形式的信任表现在网络环境中。 这种办法较少依赖中央当局，而更多地依靠通过声誉机制维持社区治安。 该项目将根据标准评估这些方法，例如给定方法的启动容易程度，帮助成员找到有能力和良好的同行的效率，以及对不值得信任的成员入侵的免疫力。 该项目还将研究总体现象，如次级社区的出现、次级社区之间的联系以及社区对成员变化的敏感性。