标题： 超越权衡曲线：用于评估匿名化和合成数据的多变量和高级风险-效用图 显示英文标题

标题： Beyond the Trade-off Curve: Multivariate and Advanced Risk-Utility Maps for Evaluating Anonymized and Synthetic Data

摘要： 对微观数据进行匿名化需要在降低披露风险和保持数据效用之间取得平衡。 传统的评估方法通常依赖于单一指标或二维的风险-效用（R-U）图，但现实世界的评估涉及多个相互关联的风险和效用指标。 这些指标的成对比较可能效率低下且不完整。 因此，我们系统地比较了六种可视化方法，以同时评估多个风险和效用指标：热图、点图、复合散点图、平行坐标图、径向轮廓图和基于主成分分析的双标图。 我们引入了分块主成分分析用于复合散点图，以及联合主成分分析用于双标图，这些方法能够同时揭示方法性能和指标之间的关系。 通过在所有方法中系统地识别帕累托最优方法，我们展示了多变量可视化如何支持更明智的匿名化方法选择。 显示英文摘要

摘要： Anonymizing microdata requires balancing the reduction of disclosure risk with the preservation of data utility. Traditional evaluations often rely on single measures or two-dimensional risk-utility (R-U) maps, but real-world assessments involve multiple, often correlated, indicators of both risk and utility. Pairwise comparisons of these measures can be inefficient and incomplete. We therefore systematically compare six visualization approaches for simultaneous evaluation of multiple risk and utility measures: heatmaps, dot plots, composite scatterplots, parallel coordinate plots, radial profile charts, and PCA-based biplots. We introduce blockwise PCA for composite scatterplots and joint PCA for biplots that simultaneously reveal method performance and measure interrelationships. Through systematic identification of Pareto-optimal methods in all approaches, we demonstrate how multivariate visualization supports a more informed selection of anonymization methods.