Towards Efficient Data Valuation Based on the Shapley Value

TL;DR

This paper explores efficient algorithms for approximating the Shapley value to determine data worth, enabling fair profit sharing and breach compensation, with practical demonstrations on benchmark datasets.

Contribution

It introduces new algorithms for fast approximation of the Shapley value in data valuation, addressing computational challenges and demonstrating their effectiveness.

Findings

Algorithms significantly reduce computation time

Effective valuation of training data instances

Applicable to various benchmark datasets

Abstract

"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.