Data Shapley is an important tool for data valuation, which quantifies the contribution of individual data points to machine learning models. In practice, group-level data valuation is desirable when data providers contribute data in batch. However, we identify that existing group-level extensions of Data Shapley are vulnerable to shell company attacks, where strategic group splitting can unfairly inflate valuations. We propose Faithful Group Shapley Value (FGSV) that uniquely defends against such attacks. Building on original mathematical insights, we develop a provably fast and accurate approximation algorithm for computing FGSV. Empirical experiments demonstrate that our algorithm significantly outperforms state-of-the-art methods in computational efficiency and approximation accuracy, while ensuring faithful group-level valuation.

翻译：数据Shapley是数据估值的重要工具，用于量化单个数据点对机器学习模型的贡献。在实践中，当数据提供者以批量方式贡献数据时，组级数据估值具有实际需求。然而，我们发现现有数据Shapley的组级扩展方法容易受到空壳公司攻击，即通过策略性分组拆分可能不公平地抬高估值。我们提出忠实组Shapley值（FGSV），该方法能独特地防御此类攻击。基于原创的数学洞见，我们开发了可证明快速且精确的近似算法来计算FGSV。实证实验表明，我们的算法在计算效率和近似精度上显著优于现有最优方法，同时确保组级估值的忠实性。