用文本嵌入聚类自动生成数据匿名化通用规则，提升隐私保护效果。
ClustEm4Ano: Clustering Text Embeddings of Nominal Textual Attributes for Microdata Anonymization
- 基于文本嵌入迭代聚类生成语义相近的值通用层级。
- 在成人数据集上验证，小k匿名性下性能优于人工构造规则。
- 适合需要高隐私保护质量的数据发布场景。
本文提出ClustEm4Ano，一种用于名义文本表格数据泛化与抑制的匿名化流程。该方法自动构建值通用层级（VGHs），用于对准标识符属性进行泛化。通过在13种预定义文本嵌入（含开源与API调用的闭源模型）上应用KMeans与层次聚类，实现语义相近的值分组。在UCI机器学习仓库的Adult数据集上进行实验验证。结果表明，相比任意设定的VGHs，该方法生成的层级在小k匿名性（2 ≤ k ≤ 30）条件下显著提升下游效用，有助于提升匿名数据集质量。代码已公开。
原文摘要 · Abstract (English)
This work introduces ClustEm4Ano, an anonymization pipeline that can be used for generalization and suppression-based anonymization of nominal textual tabular data. It automatically generates value generalization hierarchies (VGHs) that, in turn, can be used to generalize attributes in quasi-identifiers. The pipeline leverages embeddings to generate semantically close value generalizations through iterative clustering. We applied KMeans and Hierarchical Agglomerative Clustering on $13$ different predefined text embeddings (both open and closed-source (via APIs)). Our approach is experimentally tested on a well-known benchmark dataset for anonymization: The UCI Machine Learning Repository's Adult dataset. ClustEm4Ano supports anonymization procedures by offering more possibilities compared to using arbitrarily chosen VGHs. Experiments demonstrate that these VGHs can outperform manually constructed ones in terms of downstream efficacy (especially for small $k$-anonymity ($2 \leq k \leq 30$)) and therefore can foster the quality of anonymized datasets. Our implementation is made public.
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