Data in its original form, however, typically contain sensitive information about individuals. Directly publishing raw data will violate the privacy of people involed. Consequently, it becomes increasingly important to preserve the privacy of published data. An attacker is apt to identify an individual from the published tables, with attacks through the record linkage, attribute linkage, table linkage or probabilistic attack. Although algorithms based on generalization and suppression have been proposed to protect the sensitive attributes and resist these multiple types of attacks, they often suffer from large information loss by replacing specific values with more general ones. Alternatively, anatomization and permutation operations can de-link the relation between attributes without modifying them. In this paper, we propose a scheme Sensitive Label Privacy Preservation with Anatomization (SLPPA) to protect the privacy of published data. SLPPA includes two procedures, table division and group division. During the table division, we adopt entropy and mean-square contingency coefficient to partition attributes into separate tables to inject uncertainty for reconstructing the original table. During the group division, all the individuals in the original table are partitioned into non-overlapping groups so that the published data satisfies the pre-defined privacy requirements of our ( $\alpha,\beta,\gamma,\delta$ ) model. Two comprehensive sets of real-world relationship data are applied to evaluate the performance of our anonymization approach. Simulations and privacy analysis show our scheme possesses better privacy while ensuring higher utility.

中文翻译：

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带有解剖结构的敏感标签隐私保护，用于数据发布

但是，原始形式的数据通常包含有关个人的敏感信息。直接发布原始数据将侵犯参与人员的隐私。因此，保护​​已发布数据的隐私变得越来越重要。攻击者易于从已发布的表中识别个人，并通过记录链接，属性链接，表链接或概率攻击进行攻击。尽管已经提出了基于泛化和抑制的算法来保护敏感属性并抵御这些多种类型的攻击，但是通过用更通用的值替换特定的值，它们经常遭受大量信息丢失的困扰。替代地，解剖和置换操作可以使属性之间的关系去链接，而无需修改它们。在本文中，我们提出了一种带有解剖结构的敏感标签隐私保护方案（SLPPA），以保护已发布数据的隐私。SLPPA包括两个过程，表划分和组划分。在表划分过程中，我们采用熵和均方列联系数，将属性划分到单独的表中，以注入不确定性以重建原始表。在组划分期间，原始表中的所有个人都被划分为不重叠的组，以便发布的数据满足我们（我们采用熵和均方性列联系数将属性划分到单独的表中，以注入不确定性来重建原始表。在组划分期间，原始表中的所有个人都被划分为不重叠的组，以便发布的数据满足我们（我们采用熵和均方性列联系数将属性划分到单独的表中，以注入不确定性来重建原始表。在组划分期间，原始表中的所有个人都被划分为不重叠的组，以便发布的数据满足我们（ $ \ alpha，\ beta，\ gamma，\ delta $ ） 模型。应用了两套全面的现实世界关系数据来评估我们匿名方法的性能。仿真和隐私分析表明，我们的方案在确保更高实用性的同时拥有更好的隐私。