中文摘要：

针对聚类分析时如何保护隐私的问题，提出了一种称为IBT（基于等距变换的数据转换）的算法。IBT先随机选择属性向量对，然后对属性向量对进行等距变换。变换过程中，根据所要求的相对隐私保护度来确定变换角度目的选择范围，最终在符合要求的范围中随机选择变换的角度。实验结果表明，IBT能保持两点间距离不变。很好地扭曲了数据，保护隐私信息，且对聚类的结果没有影响。

英文摘要：

This paper is concentrated on the issue of protecting the underlying attribute values when sharing data for clustering and proposes a method called Isometric-Based Transformation （IBT）. IBT selects the attribute pairs and then distorts them with isometric transformation. In the process of transformation, the goal is to find the proper angle ranges to satisfy the least privacy preserving requirement and then randomly choose one angle θ in this interval. The experiments demonstrate that the method efficiently distorts attribute values, preserves privacy information and guarantees valid clustering results.