论文标题
对Deidinification防御的攻击
Attacks on Deidentification's Defenses
论文作者
论文摘要
基于准识别者的去识别技术（Qi-deidiendification）在实践中广泛使用，包括$ k $ - 匿名，$ \ ell $ -Dovertity和$ t $ -closeness。我们提出了对QI识别的三个新攻击：两次理论攻击和对真实数据集的一次实际攻击。与先前的工作相反，即使每个属性都是准识别人，我们的理论攻击也起作用。因此，它们适用于$ k $ - 匿名，$ \ ell $ - 多样性，$ t $ -closeness和大多数其他质量识别技术。 首先，我们引入了一种新的隐私攻击类，称为“下衣攻击”，并证明，如果它是最小和分层的，则每个QI识别方案都容易受到降低编码攻击的影响。其次，我们将秘密攻击转换为强大的谓词单打（PSO）攻击，最近提出了一种证明隐私机制未能根据欧洲的一般数据保护法规法律匿名化的一种方式。第三，我们使用LinkedIn.com在EDX出版的$ K $匿名数据中重新识别3名学生（并且显示了数千个可能是脆弱的），这破坏了EDX声称遵守《家庭教育权利和隐私法》。 这项工作的意义既是科学又是政治的。我们的理论攻击表明，即使每个属性都被视为准识别器，QI识别也可能没有任何保护。我们的实际攻击表明，即使是根据严格的隐私法规行事的去识别专家也无法阻止现实世界的重新识别。他们共同驳回了QI识别的基本宗旨，并挑战了继续使用$ k $匿名和其他QI识别技术的实际论点。
Quasi-identifier-based deidentification techniques (QI-deidentification) are widely used in practice, including $k$-anonymity, $\ell$-diversity, and $t$-closeness. We present three new attacks on QI-deidentification: two theoretical attacks and one practical attack on a real dataset. In contrast to prior work, our theoretical attacks work even if every attribute is a quasi-identifier. Hence, they apply to $k$-anonymity, $\ell$-diversity, $t$-closeness, and most other QI-deidentification techniques. First, we introduce a new class of privacy attacks called downcoding attacks, and prove that every QI-deidentification scheme is vulnerable to downcoding attacks if it is minimal and hierarchical. Second, we convert the downcoding attacks into powerful predicate singling-out (PSO) attacks, which were recently proposed as a way to demonstrate that a privacy mechanism fails to legally anonymize under Europe's General Data Protection Regulation. Third, we use LinkedIn.com to reidentify 3 students in a $k$-anonymized dataset published by EdX (and show thousands are potentially vulnerable), undermining EdX's claimed compliance with the Family Educational Rights and Privacy Act. The significance of this work is both scientific and political. Our theoretical attacks demonstrate that QI-deidentification may offer no protection even if every attribute is treated as a quasi-identifier. Our practical attack demonstrates that even deidentification experts acting in accordance with strict privacy regulations fail to prevent real-world reidentification. Together, they rebut a foundational tenet of QI-deidentification and challenge the actual arguments made to justify the continued use of $k$-anonymity and other QI-deidentification techniques.
