We construct efficient non-malleable codes (NMC) that are (computationally) secure against tampering by functions computable in any fixed polynomial time. Our construction is in the plain (no-CRS) model and requires the assumptions that (1) E is hard for NP circuits of some exponential 2 βn (β> 0) size (widely used in the derandomization literature), (2) sub-exponential trapdoor permutations exist, and (3) P -certificates with sub-exponential soundness exist. While it is impossible to construct NMC secure against arbitrary polynomial-time tampering (Dziembowski, Pietrzak, Wichs, ICS ’10), the existence of NMC secure against O(nc) -time tampering functions (for any fixed c), was shown (Cheraghchi and Guruswami, ITCS ’14) via a probabilistic construction. An explicit construction was given (Faust, Mukherjee, Venturi, Wichs, Eurocrypt ’14) assuming an untamperable CRS with length longer than the runtime of the tampering function. In this work, we show that under computational assumptions, we can bypass these limitations. Specifically, under the assumptions listed above, we obtain non-malleable codes in the plain model against O(nc) -time tampering functions (for any fixed c), with codeword length independent of the tampering time bound. Our new construction of NMC draws a connection with non-interactive non-malleable commitments. In fact, we show that in the NMC setting, it suffices to have a much weaker notion called quasi non-malleable commitments—these are non-interactive, non-malleable commitments in the plain model, in which the adversary runs in O(nc) -time, whereas the honest parties may run in longer (polynomial) time. We then construct a 4-tag quasi non-malleable commitment from any sub-exponential OWF and the assumption that E is hard for some exponential size NP -circuits, and use tag amplification techniques to support an exponential number of tags.
CITATION STYLE
Ball, M., Dachman-Soled, D., Kulkarni, M., Lin, H., & Malkin, T. (2019). Non-malleable codes against bounded polynomial time tampering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11476 LNCS, pp. 501–530). Springer Verlag. https://doi.org/10.1007/978-3-030-17653-2_17
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