Depending on the inherent characteristic of sensitive attributes, even those existing enhanced anonymous models still permit the private information to be disclosed or have other limitations so far, such as skewness attacks or sensitive attacks. Based on this, three novel enhanced anonymous models, i.e., $(p,{\alpha }_{isg})$-sensitive $k$-anonymity model, $(p^{+},{\alpha }_{isg})$-sensitive $k$-anonymity model and $(p_i^{+},{\alpha }_{isg})$-sensitive $k$-anonymity model, are proposed to improve the privacy preservation by fully considering the sensitivity of different sensitive values on the sensitive attribute so as to realize better personalized privacy preservation. Different from the traditional methods, which basically quantify the sensitive level of the specific sensitive value focusing on user-defined classification approaches, the sensitivity of different sensitive values based on self-information is fully considered to obtain sensitive levels partition (SLP) so as to achieve better privacy by designing SLP from qualitative status towards quantitative status in our models. The conception of identical sensitive group (ISG), which is generated from the idea of hierarchical clustering method by using SLP algorithm, is introduced to design the anonymous models for better defending against sensitive attacks. In this case, the sensitive values with the most similar sensitivity are most likely clustered in the same ISG. Moreover, the frequency of each ISG is confined to no more than the specific personalized threshold ${\alpha }_{isg}$ in any equivalence class without ending up with “one size for all” restrictions. Here, higher sensitive of ISG should be assigned a lower frequency constraint for resisting sensitive attacks so as to achieve better privacy. Then, two clustering algorithms are devised by using the idea of bottom-up greedy methods. Experiment results based on two real-world datasets show that our three anonymous models can effectively protect data privacy and enhance data security and practicality with certain information loss.

根据敏感属性的固有特征，即使是那些现有的增强匿名模型，仍然允许私人信息被泄露或具有其他局限性，例如偏斜攻击或敏感攻击。基于此，三个新颖的增强匿名模型，即$（p，{\alpha }_{\mathrm{一世}sG}）$-敏感 $ķ$-匿名模型， $（p^{+}，{\alpha }_{\mathrm{一世}sG}）$-敏感 $ķ$-匿名模型和 $（p_{\mathrm{一世}}^{+}，{\alpha }_{\mathrm{一世}sG}）$-敏感 $ķ$提出了一种匿名模型，通过充分考虑不同敏感值对敏感属性的敏感度来改善隐私保护，从而实现更好的个性化隐私保护。与传统方法（基本上是基于用户定义的分类方法来量化特定敏感值的敏感级别）不同，充分考虑了基于自我信息的不同敏感值的敏感度以获得敏感级别划分（SLP），从而通过在模型中设计从定性状态到定量状态的SLP，可以获得更好的隐私。相同敏感组（ISG）的概念是通过使用SLP算法的分层聚类方法的思想而产生的，引入以设计匿名模型以更好地防御敏感攻击。在这种情况下，具有最相似灵敏度的敏感值很可能聚集在同一ISG中。此外，每个ISG的频率限制为不超过特定的个性化阈值${\alpha }_{\mathrm{一世}sG}$在任何等价类中，都没有以“所有人都有一个统一的尺寸”的限制而告终。在此，应为ISG的较高敏感度分配较低的频率限制以抵抗敏感攻击，以实现更好的隐私。然后，利用自下而上的贪婪方法的思想，设计了两种聚类算法。基于两个真实数据集的实验结果表明，我们的三个匿名模型可以有效地保护数据隐私，并在某些信息丢失的情况下提高数据安全性和实用性。