Syta, E.; Jovanovic, P.; Kokoris Kogias, LefterisISTA; Gailly, N.; Gasser, L.; Khoffi, I.; Fischer, M. J.; Ford, B.
Bias-resistant public randomness is a critical component in many (distributed) protocols. Generating public randomness is hard, however, because active adversaries may behave dishonestly to bias public random choices toward their advantage. Existing solutions do not scale to hundreds or thousands of participants, as is needed in many decentralized systems. We propose two large-scale distributed protocols, RandHound and RandHerd, which provide publicly-verifiable, unpredictable, and unbiasable randomness against Byzantine adversaries. RandHound relies on an untrusted client to divide a set of randomness servers into groups for scalability, and it depends on the pigeonhole principle to ensure output integrity, even for non-random, adversarial group choices. RandHerd implements an efficient, decentralized randomness beacon. RandHerd is structurally similar to a BFT protocol, but uses RandHound in a one-time setup to arrange participants into verifiably unbiased random secret-sharing groups, which then repeatedly produce random output at predefined intervals. Our prototype demonstrates that RandHound and RandHerd achieve good performance across hundreds of participants while retaining a low failure probability by properly selecting protocol parameters, such as a group size and secret-sharing threshold. For example, when sharding 512 nodes into groups of 32, our experiments show that RandHound can produce fresh random output after 240 seconds. RandHerd, after a setup phase of 260 seconds, is able to generate fresh random output in intervals of approximately 6 seconds. For this configuration, both protocols operate at a failure probability of at most 0.08% against a Byzantine adversary.
2017 IEEE Symposium on Security and Privacy
SP: Symposium on Security and Privacy
San Jose, CA, United States
2017-05-22 – 2017-05-26
Syta E, Jovanovic P, Kokoris Kogias E, et al. Scalable bias-resistant distributed randomness. In: 2017 IEEE Symposium on Security and Privacy. IEEE; 2017:444-460. doi:10.1109/SP.2017.45
Syta, E., Jovanovic, P., Kokoris Kogias, E., Gailly, N., Gasser, L., Khoffi, I., … Ford, B. (2017). Scalable bias-resistant distributed randomness. In 2017 IEEE Symposium on Security and Privacy (pp. 444–460). San Jose, CA, United States: IEEE. https://doi.org/10.1109/SP.2017.45
Syta, E., P. Jovanovic, Eleftherios Kokoris Kogias, N. Gailly, L. Gasser, I. Khoffi, M. J. Fischer, and B. Ford. “Scalable Bias-Resistant Distributed Randomness.” In 2017 IEEE Symposium on Security and Privacy, 444–60. IEEE, 2017. https://doi.org/10.1109/SP.2017.45.
E. Syta et al., “Scalable bias-resistant distributed randomness,” in 2017 IEEE Symposium on Security and Privacy, San Jose, CA, United States, 2017, pp. 444–460.
Syta E, Jovanovic P, Kokoris Kogias E, Gailly N, Gasser L, Khoffi I, Fischer MJ, Ford B. 2017. Scalable bias-resistant distributed randomness. 2017 IEEE Symposium on Security and Privacy. SP: Symposium on Security and Privacy, 444–460.
Syta, E., et al. “Scalable Bias-Resistant Distributed Randomness.” 2017 IEEE Symposium on Security and Privacy, IEEE, 2017, pp. 444–60, doi:10.1109/SP.2017.45.