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