Asymptotic fingerprinting capacity in the combined digit model

We study the channel capacity of q-ary fingerprinting in the limit of large attacker coalitions. We extend known results by considering the Combined Digit Model, an attacker model that captures signal processing attacks such as averaging and noise addition. For q = 2 we give results for various attack parameter settings

Anticollusion solutions for asymmetric fingerprinting protocols based on client side embedding

In this paper, we propose two different solutions for making a recently proposed asymmetric fingerprinting protocol based on client-side embedding robust to collusion attacks. The first solution is based on projecting a client-owned random fingerprint, securely obtained through existing cryptographic protocols, using for each client a different random matrix generated by the server. The second solution consists in assigning to each client a Tardos code, which can be done using existing asymmetric protocols, and modulating such codes using a specially designed random matrix. Suitable accusation strategies are proposed for both solutions, and their performance under the averaging attack followed by the addition of Gaussian noise is analytically derived. Experimental results show that the analytical model accurately predicts the performance of a realistic system. Moreover, the results also show that the solution based on independent random projections outperforms the solution based on Tardos codes, for different choices of parameters and under different attack models

Asymptotic fingerprinting capacity in the combined digit model

We study the channel capacity of q-ary fingerprinting in the limit of large attacker coalitions. We extend known results by considering the Combined Digit Model, an attacker model that captures signal processing attacks such as averaging and noise addition. For q¿=¿2 we give results for various attack parameter settings. For q¿=¿3 we present the relevant equations without providing a solution. We show how the channel capacity in the Restricted Digit Model is obtained as a limiting case of the Combined Digit Model

Asymptotic fingerprinting capacity in the Combined Digit Model

We study the channel capacity of q-ary fingerprinting in the limit of large attacker coalitions. We extend known results by considering the Combined Digit Model, an attacker model that captures signal processing attacks such as averaging and noise addition. For q = 2 we give results for various attack parameter settings. For q ≥ 3 we present the relevant equations without providing a solution. We show how the channel capacity in the Restricted Digit Model is obtained as a limiting case of the Combined Digit Model.