匿名数据的效用可以用于隐私泄露吗？
Can the Utility of Anonymized Data be used for Privacy Breaches?
摘要：
基于组的匿名化是隐私保护数据发布中研究最广泛的方法。这包括k-匿名性，l-多样性和t-封闭性，仅举几例。本文的目的是提出一个基本问题，目前的群体为基础的方法的隐私暴露。这一点在过去被忽视了。基于组的匿名化方法基本上将每个单独的记录隐藏在组后面，以保护数据隐私。如果没有适当的匿名化，模式实际上可以从发布的数据中获得，并被对手用来侵犯个人隐私。例如，从发布的医疗记录中，如果可以导出诸如来自某些国家的人很少患有某种疾病的模式，则可以使用该信息来暗示匿名组中的其他人与这种疾病的联系具有更高的可能性。我们把从已发布的数据中得到的模式称为前景知识。这是在对比的背景知识，对手可能会从其他渠道获得的研究在以前的一些工作。最后，我们通过实验表明，攻击是现实的隐私基准数据集下，传统的基于组的匿名化方法。
英文摘要：
Group based anonymization is the most widely studied approach for privacy preserving data publishing. This includes k-anonymity, l-diversity, and t-closeness, to name a few. The goal of this paper is to raise a fundamental issue on the privacy exposure of the current group based approach. This has been overlooked in the past. The group based anonymization approach basically hides each individual record behind a group to preserve data privacy. If not properly anonymized, patterns can actually be derived from the published data and be used by the adversary to breach individual privacy. For example, from the medical records released, if patterns such as people from certain countries rarely suffer from some disease can be derived, then the information can be used to imply linkage of other people in an anonymized group with this disease with higher likelihood. We call the derived patterns from the published data the foreground knowledge. This is in contrast to the background knowledge that the adversary may obtain from other channels as studied in some previous work. Finally, we show by experiments that the attack is realistic in the privacy benchmark dataset under the traditional group based anonymization approach.
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。
