Improving the accuracy and stability of privacy-aware Shapley data valuation

Abstract The integration of privacy-preserving mechanisms like differential privacy into data valuation based on Shapley values introduces systematic noise, significantly degrading the accuracy and stability of estimates—a challenge not adequately addressed by existing methods. To address this, we p...

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Hlavní autoři:	Yungang Tang, Yulin Wan, Jianzhong Li
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Springer 2026-05-01
Edice:	Discover Applied Sciences
Témata:	Data valuation Shapley value Differential privacy Estimation stability
On-line přístup:	https://doi.org/10.1007/s42452-026-08711-0
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