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"How much is my data worth?" is an increasingly common question posed byorganizations and individuals alike. An answer to this question could allow,for instance, fairly distributing profits among multiple data contributors anddetermining prospective compensation when data breaches happen. In this paper,we study the problem of data valuation by utilizing the Shapley value, apopular notion of value which originated in cooperative game theory. TheShapley value defines a unique payoff scheme that satisfies many desiderata forthe notion of data value. However, the Shapley value often requires exponentialtime to compute. To meet this challenge, we propose a repertoire of efficientalgorithms for approximating the Shapley value. We also demonstrate the valueof each training instance for various benchmark datasets.

Towards Efficient Data Valuation Based on the Shapley Value