透明化：挫败了解算法的对手
Transparent Anonymization: Thwarting Adversaries Who Know the Algorithm
摘要：
已经提出了许多泛化技术用于隐私保护数据发布。然而，大多数现有技术都隐含地假设对手对数据发布者所采用的匿名化算法知之甚少。因此，它们无法防范利用匿名化机制的各种特征的隐私攻击。本文为上述问题提供了一个实用的解决方案。首先，我们提出了一个分析模型，用于评估披露风险，当对手知道一切在匿名化过程中，除了敏感值。基于此模型，我们开发了一个隐私原则，透明的l-多样性，这确保了隐私保护，对这样强大的对手。我们确定了三种算法，实现透明的l-多样性，并通过大量的实验与真实数据验证其有效性和效率。
英文摘要：
Numerous generalization techniques have been proposed for privacy preserving data publishing. Most existing techniques, however, implicitly assume that the adversary knows little about the anonymization algorithm adopted by the data publisher. Consequently, they cannot guard against privacy attacks that exploit various characteristics of the anonymization mechanism. This paper provides a practical solution to the above problem. First, we propose an analytical model for evaluating disclosure risks, when an adversary knows everything in the anonymization process, except the sensitive values. Based on this model, we develop a privacy principle, transparent l-diversity, which ensures privacy protection against such powerful adversaries. We identify three algorithms that achieve transparent l-diversity, and verify their effectiveness and efficiency through extensive experiments with real data.
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。
