TWC: Small: Assessing Online Information Exposure Using Web Footprints

这个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领域。暴露检测指的是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，该研究项目侧重于开发有效的算法，以模拟攻击者如何使用不完整和无模式的公共数据源学习信息。本文介绍了从理论上合理有效地识别准确网络足迹的技术，包括：在多颗粒数据上使用新的概率连接算子进行数据匹配的新方法，生成推理规则的自动化方法，以及使用轻量级自然语言处理和文本挖掘识别缺失信息和统一不匹配词汇的新解决方案。研究活动还调查了量化和调整暴露和风险的方法，从而更好地了解个人在网络上的脆弱性。这些技术不仅在再识别、概率推理和推理逻辑以及自然语言理解方面取得了进展，而且为暴露检测奠定了基础。

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.