III:Small:Privacy Preserving Data Publishing: A Second Look on Group based Anonymization

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。该提案的目标是提出一个关于这种方法的隐私暴露的基本问题，这个问题在过去一直被忽视，并提出一个计算效率高的解决方案。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。然而，模式仍然可以从已发布的匿名数据中导出或挖掘，并被对手用来侵犯个人隐私。 因此，本研究的目的是开发新的基于组的匿名化方法，可以抵御这种攻击。 该项目的第一部分是定义攻击问题，即，发布的匿名数据实际上可以被挖掘用于隐私攻击。它确定并制定了隐私暴露于这种攻击。第二部分是对现有隐私技术暴露于攻击的情况进行系统研究。第三部分是推导出能够抵抗这种攻击的条件，并开发有效的数据发布算法来防止这种攻击的发生。

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 proposal is to raise a fundamental issue on the privacy exposure of this approach which has been overlooked in the past and come out with a computationally efficient solution. The group based anonymization approach basically hides each individual record behind a group to preserve data privacy. However, patterns may still be derived or mined from the published anonymized data and be used by the adversary to breach individual privacy. The objective of this research is therefore to develop novel group-based anonymization methods that can defend against such an attack. The first part of the project is to define the attack problem, i.e., the published anonymized data can in fact be mined for privacy attacks. It identifies and formulates the privacy exposure to such an attack. The second part is to conduct a systematic study on the exposure of existing privacy techniques to the attack. The third part is to derive the condition that is able to resist such an attack and develop efficient data publishing algorithms to prevent it from occurring.