Weizmann Logo
ECCC
Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

Login | Register | Classic Style



REPORTS > DETAIL:

Revision(s):

Revision #1 to TR17-168 | 6th November 2017 17:47

Tighter Bounds on Multi-Party Coin Flipping, via Augmented Weak Martingales and Di erentially Private Sampling

RSS-Feed




Revision #1
Authors: Amos Beimel, Iftach Haitner, Nikolaos Makriyannis, Eran Omri
Accepted on: 6th November 2017 17:47
Downloads: 42
Keywords: 


Abstract:

In his seminal work, Cleve [STOC 1986] has proved that any $r$-round coin-flipping protocol can be efficiently biassed by $\Omega(1/r)$. The above lower bound was met for the two-party case by Moran, Naor, and Segev [Journal of Cryptology '16], and the three-party case (up to a polylog factor) by Haitner and Tsfadia [SICOMP '17], and was approached for $n$-party protocols when $n \loglog r$, the best bias for $n$-party coin-flipping protocols remains $\Theta(n/\sqrt{r})$ achieved by the majority protocol of Awerbuch, Blum, Chor, Goldwasser, and Micali [Manuscript '85].

Our main result is a tighter lower bound on the bias of coin-flipping protocols, showing that, for every constant $\epsilon$ > 0, an $r^{\epsilon}$-party r-round coin-flipping protocol can be efficiently biased by $\Omega(1/\sqrt{r})$. As far as we know, this is the rst improvement of Cleve's bound that holds in the standard model, and is only $r^{\epsilon}$ (multiplicative) far from the aforementioned upper bound of Awerbuch et al.

For proving the above lower bound, we present 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 the result of Cleve and Impagliazzo [Manuscript '93], who proved that the above holds for strong martingales. The second result is a new sampling algorithm that uses a differentially private mechanism to minimize the effect of data divergence.



Changes to previous version:

Fixed typos in abstract


Paper:

TR17-168 | 5th November 2017 17:12

Tighter Bounds on Multi-Party Coin Flipping, via Augmented Weak Martingales and Di erentially Private Sampling





TR17-168
Authors: Amos Beimel, Iftach Haitner, Nikolaos Makriyannis, Eran Omri
Publication: 5th November 2017 17:31
Downloads: 102
Keywords: 


Abstract:

In his seminal work, Cleve [STOC 1986] has proved that any r-round coin-flipping protocol can be efficiently biassed by ?(1/r). The above lower bound was met for the two-party case by Moran, Naor, and Segev [Journal of Cryptology '16], and the three-party case (up to a polylog factor) by Haitner and Tsfadia [SICOMP '17], and was approached for n-party protocols when n loglog r, however, the best bias for n-party coin-flipping protocols remains ?(n/?r) achieved by the majority protocol of Awerbuch, Blum, Chor, Goldwasser, and Micali [Manuscript '85].

Our main result is a tighter lower bound on the bias of coin-flipping protocols, showing that, for every constant ? > 0, an r?-party r-round coin-flipping protocol can be efficiently biased by ?(1/?r). As far as we know, this is the rst improvement of Cleve's bound that holds in the standard model, and is only r? (multiplicative) far from the aforementioned upper bound of Awerbuch et al.

For proving the above lower bound, we present 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 the result of Cleve and Impagliazzo [Manuscript '93], who proved that the above holds for strong martingales. The second result is a new sampling algorithm that uses a differentially private mechanism to minimize the effect of data divergence.



ISSN 1433-8092 | Imprint