Tighter Bounds on Multi-Party Coin Flipping via Augmented Weak Martingales and Differentially Private Sampling

In his seminal work, Cleve [STOC '86] proved that the bias of any coin-flipping protocol is inversely proportional to the number of rounds. This lower bound was met for the two-party case by Moran et al. [Journal of Cryptology '16], and the three-party case (up to a polylogarithmic factor) by Haitner and Tsfadia [SICOMP '17], and was approached for multi-party protocols by Haitner et al. [SODA '17] when the number of rounds is at least doubly exponential in the number of parties. For the complement case, however, the best bias for multi-party coin-flipping protocols is proportional to the number of parties and inversely proportional to the square root of the number of rounds. The latter bias is achieved by the majority protocol of Awerbuch et al. [Manuscript '85]. Our main result is a tighter lower bound on the bias of coin-flipping protocols, showing that, if the number of rounds is bounded by some polynomial in the number of parties, then the bias is lower-bounded by a quantity that is inversely proportional to the square root of the number of rounds (up to a polylogarithmic factor). As far as we know, this is the first improvement of Cleve's bound, and is far from the aforementioned upper bound of Awerbuch et al. only by a factor of the number of parties. We prove the above bound using two new results that we believe are of independent interest. The first result is that a sequence of ("augmented") weak martingales have large gap: with constant probability there exists two adjacent variables whose gap is at least the ratio between the gap between the first and last variables and the square root of the number of variables. This generalizes over the result of Cleve and Impagliazzo [Manuscript '93], who showed that the above holds for strong martingales, and allows in some setting to exploit this gap by efficient algorithms. We prove the above using a novel argument that does not follow the more complicated approach of Cleve and Impagliazzo. The second result is a new sampling algorithm that uses a differentially private mechanism to minimize the effect of data divergence.