Achievable Error Exponents for the Private Fingerprinting Game

Abstract

Fingerprinting systems in the presence of collusive attacks are analyzed as a game between a fingerprinter and a decoder on the one hand, and a coalition of two or more attackers on the other hand. The fingerprinter distributes, to different users, different fingerprinted copies of a host data (covertext), drawn from a memoryless stationary source, embedded with different fingerprints. The coalition members create a forgery of the data while aiming at erasing the fingerprints in order not to be detected. Their action is modeled by a multiple-access channel (MAC). We analyze the performance of two classes of decoders, associated with different kinds of error events. The decoder of the first class aims at detecting the entire coalition, whereas the second is satisfied with the detection of at least one member of the coalition. Both decoders have access to the original covertext data and observe the forgery in order to identify member(s) of the coalition. Motivated by a worst case approach, we assume that the coalition of attackers is informed of the hiding strategy taken by the fingerprinter and the decoder, while they are uninformed of the attacking scheme. Achievable single-letter expressions for the two kinds of error exponents are obtained. Single-letter lower bounds are also derived for the subclass of constant composition codes. These lower and the upper bounds coincide for the error exponent of the first class. Further, for the error of the first kind, a decoder that is optimal is introduced, and the worst case attack channel is characterized