Paper detail

TRAP: The Bait of Rational Players to Solve Byzantine Consensus

It is impossible to solve the Byzantine consensus problem in an open network of $n$ participants if only $2n/3$ or less of them are correct. As blockchains need to solve consensus, one might think that blockchains need more than $2n/3$ correct participants. But it is yet unknown whether consensus can be solved when less than $2n/3$ participants are correct and $k$ participants are rational players, which misbehave if they can gain the loot. Trading correct participants for rational players may not seem helpful to solve consensus since rational players can misbehave whereas correct participants, by definition, cannot. In this paper, we show that consensus is actually solvable in this model, even with less than $2n/3$ correct participants. The key idea is a baiting strategy that lets rational players pretend to misbehave in joining a coalition but rewards them to betray this coalition before the loot gets stolen. We propose TRAP, a protocol that builds upon recent advances in the theory of accountability to solve consensus as soon as $n>\max\bigl(\frac{3}{2}k+3t,2(k+t)\bigr)$: by assuming that private keys cannot be forged, this protocol is an equilibrium where no coalition of $k$ rational players can coordinate to increase their expected utility regardless of the arbitrary behavior of up to $t$ Byzantine players. Finally, we show that a baiting strategy is necessary and sufficient to solve this, so-called rational agreement problem. First, we show that it is impossible to solve this rational agreement problem without implementing a baiting strategy. Second, the existence of TRAP demonstrates the sufficiency of the baiting strategy. Our TRAP protocol finds applications in blockchains to prevent players from disagreeing, that could otherwise lead to "double spending".

preprint2022arXivOpen access

Signal facts

What is known right now

Open access2 authors2 topics

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.