Data sharing ecosystems connect providers, consumers, and intermediaries to facilitate the exchange and use of data for a wide range of downstream tasks. In sensitive domains such as healthcare, privacy is enforced as a hard constraint, any shared data must satisfy a minimum privacy threshold. However, among all masking configurations that meet this requirement, the utility of the masked data can vary significantly, posing a key challenge: how to efficiently select the optimal configuration that preserves maximum utility. This paper presents Aegis, a middleware framework that selects optimal masking configurations for machine learning datasets with features and class labels. Aegis incorporates a utility optimizer that minimizes predictive utility deviation, quantifying shifts in feature label correlations due to masking. Our framework leverages limited data summaries (such as 1D histograms) or none to estimate the feature label joint distribution, making it suitable for scenarios where raw data is inaccessible due to privacy restrictions. To achieve this, we propose a joint distribution estimator based on iterative proportional fitting, which allows supporting various feature label correlation quantification methods such as mutual information, chi square, or g3. Our experimental evaluation of real world datasets shows that Aegis identifies optimal masking configurations over an order of magnitude faster, while the resulting masked datasets achieve predictive performance on downstream ML tasks on par with baseline approaches and complements privacy anonymization data masking techniques.

翻译：数据共享生态系统连接数据提供方、消费方与中介方，以促进数据交换并支持广泛的下游任务。在医疗等敏感领域，隐私被作为硬性约束强制执行，任何共享数据必须满足最低隐私阈值。然而，在所有满足该要求的脱敏配置中，脱敏后数据的效用可能存在显著差异，这带来了一个关键挑战：如何高效选择能保留最大效用的最优配置。本文提出Aegis，一个为包含特征和类别标签的机器学习数据集选择最优脱敏配置的中间件框架。Aegis集成了一个效用优化器，通过量化因脱敏导致的特征-标签相关性变化，最小化预测效用偏差。我们的框架利用有限的数据摘要（如一维直方图）或无需原始数据即可估计特征-标签联合分布，适用于因隐私限制无法访问原始数据的场景。为此，我们提出一种基于迭代比例拟合的联合分布估计器，可支持多种特征-标签相关性量化方法，如互信息、卡方检验或g3。我们在真实数据集上的实验评估表明，Aegis能以超过一个数量级的速度识别最优脱敏配置，同时所得脱敏数据集在下游机器学习任务中的预测性能与基线方法相当，并可补充隐私匿名化数据脱敏技术。