Fair Delivery of Decentralised Randomness Beacon

. The security of many protocols such as voting and blockchains relies on a secure source of randomness. Decentralised Randomness Beacon (DRB) has been considered as a promising approach, where a set of participants jointly generates a sequence of random outputs. While the DRBs have been extensively studied, they failed to capture the advantage that some participants learn random outputs earlier than other participants. In time-sensitive protocols whose execution depends on the randomness from a DRB, such an advantage allows the adversary to behave adaptively according to random outputs, compromising the fairness and/or security in these protocols. In this paper, we formalise a new property, delivery-fairness , to quan-tify the advantage. In particular, we distinguish two aspects of delivery-fairness, namely length-advantage , i.e., how many random outputs an adversary can learn earlier than correct participants, and time-advantage , i.e., how much time an adversary can learn a given random output earlier than correct participants. In addition, we prove the lower bound of delivery-fairness showing optimal guarantee. We further analyse the delivery-fairness guarantee of state-of-the-art DRBs and discuss insights, which, we show through case studies, could help improve delivery-fairness of existing systems to its optimal.