WORST CASE ATTACKS AGAINST BINARY PROBABILISTIC TRAITOR TRACING CODES
Résumé
This article deals with traitor tracing which is also known as active fingerprinting, content serialization, or user forensics. We study the impact of worst case attacks on the well-known Tardos binary prob- abilistic traitor tracing code, and especially its optimum setups re- cently advised by Amiri and Tardos, and by Huang and Moulin. This paper assesses that these optimum setups are robust in the sense that a discrepancy between the foreseen numbers of colluders and the its actual value doesn't spoil the achievable rate of a joint decoder. On the other hand, this discrepancy might have a dramatic impact on a single decoder. Since the complexity of the today's joint decoder is prohibitive, this paper mitigates the interest of the optimum setups.
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