Extracting Computational Entropy and Learning Noisy Linear Functions
COCOON '09 Proceedings of the 15th Annual International Conference on Computing and Combinatorics
Deterministic extractors for independent-symbol sources
IEEE Transactions on Information Theory
Deterministic extractors for independent-symbol sources
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part I
Generalized strong extractors and deterministic privacy amplification
IMA'05 Proceedings of the 10th international conference on Cryptography and Coding
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We study the problem of deterministically extracting almost perfect random bits from multiple weakly random sources that are mutually independent. With two independent sources, we have an explicit extractor which can extract a number of random bits that matches the best construction currently known, via the generalized leftover hash lemma. We also extend our construction to extract randomness from more independent sources. One nice feature is that the extractor still works even with all but one source exposed. Finally, we apply our extractor for a cryptographic task in which a group of parties wants to agree on a secret key for group communication over an insecure channel, without using ideal local randomness.