EAGER: Interfaces to Reduce Human Error in Social Network Access Control Policy Authoring

EAGER：减少社交网络访问控制策略编写中人为错误的接口

• 批准号: 1048846
• 负责人: Shriram Krishnamurthi
• 金额: $ 10万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048846&HistoricalAwards=false
• 关键词: EAGER; Interfaces; Reduce; Human; Error

The growth of the Internet means everyone from system administrators to casual users are regularly confronted with making decisions on how to share data. Research suggests that even experts struggle to make these decisions accurately using current access-control mechanisms. As users start to share information across social and professional applications, usable access-control mechanisms that help prevent such semantic errors are all the more urgent. This project develops an interactive authoring paradigm that helps users identify and clarify policy inconsistencies before they turn into semantic errors. The envisioned tools examine policies and their consequences during authoring, alert users to potential inconsistencies (such as isolated documents shared more globally than most others), and ask questions in order to eliminate ambiguities.The challenge in building such proactive authoring tools lies in knowing what inconsistencies and issues to track without overwhelming a user with too much interaction. The proposal therefore combines user studies with tool building and evaluation. The intellectual merit of this project lies in its marriage of research on user behavior and logical tools to produce a new paradigm of policy authoring. Broader impacts come from building tools for mainstream end-users, guided by ethnographic studies of social-network users. For further information see the project web site at the URL: http://www.margrave-tool.org/

互联网的发展意味着，从系统管理员到临时用户，每个人都经常面临如何共享数据的决策。研究表明，即使是专家也很难使用当前的访问控制机制准确地做出这些决定。随着用户开始在社交和专业应用程序之间共享信息，有助于防止此类语义错误的可用访问控制机制变得更加紧迫。该项目开发了一个交互式创作范例，帮助用户在策略不一致转变为语义错误之前识别和澄清它们。设想的工具在创作期间检查策略及其后果，提醒用户潜在的不一致(例如，与大多数其他文档相比，孤立的文档在全球范围内共享得更多)，并提出问题以消除歧义。构建此类主动创作工具的挑战在于，在不让用户因太多交互而不堪重负的情况下，了解要跟踪哪些不一致和问题。因此，该提案将用户研究与工具制作和评价结合起来。这个项目的智力价值在于它结合了对用户行为的研究和逻辑工具，以产生一种新的政策创作范式。更广泛的影响来自于为主流最终用户开发工具，并以对社交网络用户的人种学研究为指导。欲了解更多信息，请访问项目网站：http://www.margrave-tool.org/