Optimal public key traitor tracing scheme in non-black box model

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Abstract

In the context of secure content distribution, the content is encrypted and then broadcasted in a public channel, each legitimate user is provided a decoder and a secret key for decrypting the received signals. One of the main threat for such a system is that the decoder can be cloned and then sold out with the pirate secret keys. Traitor tracing allows the authority to identify the malicious users (are then called traitors) who successfully collude to build pirate decoders and pirate secret keys. This primitive is introduced by Chor, Fiat and Naor in '94 and a breakthrough in construction is given by Boneh and Franklin at Crypto '99 in which they consider three models of traitor tracing: non-black-box tracing model, single-key black box tracing model, and general black box tracing model. Beside the most important open problem of obtimizing the black-box tracing, Boneh-Franklin also left an open problem concerning non-black-box tracing, by mentioning: "it seems reasonable to believe that there exists an efficient public key traitor tracing scheme that is completely collusion resistant. In such a scheme, any number of private keys cannot be combined to form a new key. Similarly, the complexity of encryption and decryption is independent of the size of the coalition under the pirate's control. An efficient construction for such a scheme will provide a useful solution to the public key traitor tracing problem". As far as we know, this problem is still open. In this paper, we resolve this question in the affirmative way, by constructing a very efficient scheme with all parameters are of constant size and in which the full collusion of traitors cannot produce a new key. Our proposed scheme is moreover dynamic. © 2013 Springer-Verlag Berlin Heidelberg.

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APA

Guillot, P., Nimour, A., Phan, D. H., & Trinh, V. C. (2013). Optimal public key traitor tracing scheme in non-black box model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7918 LNCS, pp. 140–155). Springer Verlag. https://doi.org/10.1007/978-3-642-38553-7_8

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