别让谷歌知道我很孤独！
Don't let Google know I'm lonely!
摘要：
从购买书籍到寻找完美的伴侣，我们通过最喜欢的在线系统分享我们最私密的愿望和需求。但是，面对个人侧写，我们应该在多大程度上接受隐私承诺？特别是，我们问我们如何才能提高检测敏感的主题分析在线系统？我们提出了一个隐私泄露的定义，我们称之为ε-不可否认性，我们构建了可扩展的，实用的工具来评估对手的学习潜力。我们使用公开可用的资源展示了我们的结果，在我们的实验中检测到一系列敏感主题的学习率超过98%。
英文摘要：
From buying books to finding the perfect partner, we share our most intimate wants and needs with our favourite online systems. But how far should we accept promises of privacy in the face of personal profiling? In particular we ask how can we improve detection of sensitive topic profiling by online systems? We propose a definition of privacy disclosure we call ε-indistinguishability from which we construct scalable, practical tools to assess an adversaries learning potential. We demonstrate our results using openly available resources, detecting a learning rate in excess of 98% for a range of sensitive topics during our experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
