On the Saddle-point Solution and the Large-Coalition Asymptotics of Fingerprinting Games

TL;DR

This paper reformulates the fingerprinting game under the Boneh-Shaw assumption as a saddle-point problem, deriving the asymptotic capacity decay rate and confirming the optimality of Tardos' distribution.

Contribution

It introduces a saddle-point formulation for the fingerprinting capacity under the Boneh-Shaw assumption and rigorously derives the asymptotic capacity for large coalitions.

Findings

Capacity decays quadratically with coalition size k

Asymptotic capacity is 1/(k^2 2ln2)

Tardos' arcsine distribution asymptotically maximizes mutual information

Abstract

We study a fingerprinting game in which the number of colluders and the collusion channel are unknown. The encoder embeds fingerprints into a host sequence and provides the decoder with the capability to trace back pirated copies to the colluders. Fingerprinting capacity has recently been derived as the limit value of a sequence of maximin games with mutual information as their payoff functions. However, these games generally do not admit saddle-point solutions and are very hard to solve numerically. Here under the so-called Boneh-Shaw marking assumption, we reformulate the capacity as the value of a single two-person zero-sum game, and show that it is achieved by a saddle-point solution. If the maximal coalition size is k and the fingerprinting alphabet is binary, we show that capacity decays quadratically with k. Furthermore, we prove rigorously that the asymptotic capacity is 1/(k^2 2ln2) and we confirm our earlier conjecture that Tardos' choice of the arcsine distribution asymptotically maximizes the mutual information payoff function while the interleaving attack minimizes it. Along with the asymptotic behavior, numerical solutions to the game for small k are also presented.