DOI: 10.1145/3639454 ISSN: 0004-5411

Byzantine Agreement with Optimal Resilience via Statistical Fraud Detection

Shang-En Huang, Seth Pettie, Leqi Zhu
  • Artificial Intelligence
  • Hardware and Architecture
  • Information Systems
  • Control and Systems Engineering
  • Software

Since the mid-1980s it has been known that Byzantine Agreement can be solved with probability 1 asynchronously, even against an omniscient, computationally unbounded adversary that can adaptively corrupt up to f < n /3 parties. Moreover, the problem is insoluble with fn /3 corruptions. However, Bracha’s [13] 1984 protocol (see also Ben-Or [8]) achieved f < n /3 resilience at the cost of exponential expected latency 2 Θ ( n ) , a bound that has never been improved in this model with f = ⌊( n − 1)/3⌋ corruptions.

In this paper, we prove that Byzantine Agreement in the asynchronous, full information model can be solved with probability 1 against an adaptive adversary that can corrupt f < n /3 parties, while incurring only polynomial latency with high probability . Our protocol follows an earlier polynomial latency protocol of King and Saia [33,34], which had suboptimal resilience, namely fn /10 9  [33,34].

Resilience f = ( n − 1)/3 is uniquely difficult, as this is the point at which the influence of the Byzantine and honest players are of roughly equal strength. The core technical problem we solve is to design a collective coin-flipping protocol that eventually lets us flip a coin with an unambiguous outcome. In the beginning, the influence of the Byzantine players is too powerful to overcome, and they can essentially fix the coin’s behavior at will. We guarantee that after just a polynomial number of executions of the coin-flipping protocol, either (a) the Byzantine players fail to fix the behavior of the coin (thereby ending the game) or (b) we can “blacklist” players such that the blacklisting rate for Byzantine players is at least as large as the blacklisting rate for good players. The blacklisting criterion is based on a simple statistical test of fraud detection .

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