Many data mining applications such as clustering and $k$k-NN search rely on distances and relations in the data. Thus, distance preserving transformations, which perturb the data but retain records’ distances, have emerged as a prominent privacy protection method. In this paper, we present a novel attack on a generalized form of distance preserving transformations, called relation preserving transformations. Our attack exploits not the exact distances between data, but the relationships between the distances. We show that an attacker with few known samples (4 to 10) and direct access to relations can retrieve unknown data records with more than 95 percent precision. In addition, experiments demonstrate that simple methods of noise addition or perturbation are not sufficient to prevent our attack, as they decrease precision by only 10 percent.

中文翻译：

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关系保留数据转换的已知样本攻击

许多数据挖掘应用程序，例如聚类和 $千$克-NN 搜索依赖于数据中的距离和关系。因此，扰动数据但保留记录距离的距离保持变换已成为一种突出的隐私保护方法。在本文中，我们提出了一种对距离保持变换的广义形式的新攻击，称为关系保持变换。我们的攻击不是利用数据之间的确切距离，而是利用距离之间的关系。我们表明，具有很少已知样本（4 到 10 个）和直接访问关系的攻击者可以以超过 95% 的精度检索未知数据记录。此外，实验表明，简单的噪声添加或扰动方法不足以阻止我们的攻击，因为它们只会将精度降低 10%。