在线评论网站的隐私

M. Burkholder, R. Greenstadt
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引用次数: 0

摘要

越来越多的在线评论网站给用户隐私带来了新的挑战。虽然评论是公开的,但许多用户无意中向外界透露了有关关系、位置和时间属性的私人信息。该研究通过三种方式保护在线评论网站的用户免受私人信息的无意泄露。首先,对在线评论网站公开的非结构化和结构化信息的类型进行了特征描述,并用于对这些网站对隐私的关注进行评级。其次,提出了一种使用关键字匹配和命名实体识别来注释潜在敏感评论文本的隐私检查工具。第三,我们通过隐私检查工具的例子和统计数据来提高人们对在线评论网站隐私威胁的认识。
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Privacy in Online Review Sites
The increasing use of online review sites is creating new challenges for user privacy. Although reviews are public, many users inadvertently disclose private information about relationship, location, and temporal attributes to the world. This research protects users of online review sites from the inadvertent disclosure of private information in three ways. First, the types of unstructured and structured information made public by online review sites are characterized and used to grade those sites on their attention to privacy. Second, a privacy-check tool that uses keyword matching and named-entity recognition to annotate potentially sensitive review text is presented. Third, we raise awareness of the privacy threat in online review sites through examples and statistics derived from the privacy-check tool.
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