TWC: Small: Assessing Online Information Exposure Using Web Footprints

TWC：小：使用网络足迹评估在线信息暴露

• 批准号: 1223825
• 负责人: Lisa Singh
• 金额: $ 50万
• 依托单位: Georgetown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-15 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1223825&HistoricalAwards=false
• 关键词: TWC; Small; Assessing; Online; Information

This research project studies a new area of research - exposure detection - that is at the intersection of data mining, security, and natural language processing. Exposure detection refers to discovering components/attributes of a user's public profile that reduce the user's privacy. To help the public understand the privacy risks of sharing certain information on the web, this research project focuses on developing efficient algorithms for modeling how an adversary learns information using incomplete and schemaless public data sources. Theoretically sound and efficient techniques for identifying accurate web footprints are introduced, including: new methods for data matching using a novel probabilistic join operator on multi-granular data, automated approaches for generating inference rules, and new solutions for identifying missing information and unifying mismatched vocabulary using lightweight natural language processing and text mining. The research activities also investigate methods for quantifying and adjusting exposure and risk, facilitating a better understanding of individuals' vulnerability on the web. These techniques not only advance the state of the art in re-identification, probabilistic reasoning and inference logic, and natural language understanding, but also serve as a foundation for exposure detection.

这项研究项目研究了一个新的研究领域-暴露检测-这是数据挖掘、安全和自然语言处理的交叉点。暴露检测是指发现用户公共配置文件中降低用户隐私的组件/属性。为了帮助公众了解在网络上共享某些信息的隐私风险，该研究项目专注于开发高效的算法，以模拟对手如何使用不完整和无模式的公共数据源学习信息。介绍了理论上可靠和高效的Web足迹识别技术，包括：在多粒度数据上使用新的概率连接算子进行数据匹配的新方法，自动生成推理规则的方法，以及使用轻量级自然语言处理和文本挖掘识别遗漏信息和统一错配词汇的新解决方案。研究活动还调查量化和调整暴露和风险的方法，以促进更好地了解个人在网络上的脆弱性。这些技术不仅在重新识别、概率推理和推理逻辑以及自然语言理解方面取得了进展，而且还作为暴露检测的基础。