基于局部泛化和桶化的个性化隐私保护技术
Local Generalization and Bucketization Technique for Personalized Privacy Preservation
摘要：
匿名化技术在隐私保护数据发布中得到了广泛的研究和应用。在大多数以前的方法中，微数据表由三类属性组成：显式标识符，准标识符（QI）和敏感属性。事实上，不同的人对不同属性的敏感性可能有不同的看法。因此，存在另一种同时包含QI值和敏感值的属性，即半敏感属性。基于这样的观察，我们提出了一种新的匿名化技术，称为局部泛化和桶化，以防止身份泄露，并保护每个半敏感属性和敏感属性上的敏感值。其基本原理是使用局部泛化和局部桶化将元组划分为局部等价组，并将敏感值分别划分为局部桶。局部泛化保护和局部桶化保护是独立的，因此它们可以分别由适当的算法实现，而不会削弱其他保护。此外，每个半敏感属性和敏感属性的局部桶化保护也是独立的。因此，局部桶化可以根据匿名化的实际需求，在不同的属性下遵循不同的原则。大量的实验结果表明了该方法的有效性。
英文摘要：
Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. In most previous approaches, a microdata table consists of three categories of attribute: explicit-identifier, quasi-identifier (QI), and sensitive attribute. Actually, different individuals may have different view on the sensitivity of different attributes. Therefore, there is another type of attribute that contains both QI values and sensitive values, namely, semi-sensitive attribute. Based on such observation, we propose a new anonymization technique, called local generalization and bucketization, to prevent identity disclosure and protect the sensitive values on each semi-sensitive attribute and sensitive attribute. The rationale is to use local generalization and local bucketization to divide the tuples into local equivalence groups and partition the sensitive values into local buckets, respectively. The protections of local generalization and local bucketization are independent, so that they can be implemented by appropriate algorithms without weakening other protection, respectively. Besides, the protection of local bucketization for each semi-sensitive attribute and sensitive attribute is also independent. Consequently, local bucketization can comply with various principles in different attributes according to the actual requirements of anonymization. The conducted extensive experiments illustrate the effectiveness of the proposed approach.
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