9 research outputs found
Rational Fair Consensus in the GOSSIP Model
The \emph{rational fair consensus problem} can be informally defined as
follows. Consider a network of (selfish) \emph{rational agents}, each of
them initially supporting a \emph{color} chosen from a finite set .
The goal is to design a protocol that leads the network to a stable
monochromatic configuration (i.e. a consensus) such that the probability that
the winning color is is equal to the fraction of the agents that initially
support , for any . Furthermore, this fairness property must
be guaranteed (with high probability) even in presence of any fixed
\emph{coalition} of rational agents that may deviate from the protocol in order
to increase the winning probability of their supported colors. A protocol
having this property, in presence of coalitions of size at most , is said to
be a \emph{whp\,--strong equilibrium}. We investigate, for the first time,
the rational fair consensus problem in the GOSSIP communication model where, at
every round, every agent can actively contact at most one neighbor via a
\emph{pushpull} operation. We provide a randomized GOSSIP protocol that,
starting from any initial color configuration of the complete graph, achieves
rational fair consensus within rounds using messages of
size, w.h.p. More in details, we prove that our protocol is a
whp\,--strong equilibrium for any and, moreover, it
tolerates worst-case permanent faults provided that the number of non-faulty
agents is . As far as we know, our protocol is the first solution
which avoids any all-to-all communication, thus resulting in message
complexity.Comment: Accepted at IPDPS'1
Betrayal, Distrust, and Rationality: Smart Counter-Collusion Contracts for Verifiable Cloud Computing
Cloud computing has become an irreversible trend. Together comes the pressing
need for verifiability, to assure the client the correctness of computation
outsourced to the cloud. Existing verifiable computation techniques all have a
high overhead, thus if being deployed in the clouds, would render cloud
computing more expensive than the on-premises counterpart. To achieve
verifiability at a reasonable cost, we leverage game theory and propose a smart
contract based solution. In a nutshell, a client lets two clouds compute the
same task, and uses smart contracts to stimulate tension, betrayal and distrust
between the clouds, so that rational clouds will not collude and cheat. In the
absence of collusion, verification of correctness can be done easily by
crosschecking the results from the two clouds. We provide a formal analysis of
the games induced by the contracts, and prove that the contracts will be
effective under certain reasonable assumptions. By resorting to game theory and
smart contracts, we are able to avoid heavy cryptographic protocols. The client
only needs to pay two clouds to compute in the clear, and a small transaction
fee to use the smart contracts. We also conducted a feasibility study that
involves implementing the contracts in Solidity and running them on the
official Ethereum network.Comment: Published in ACM CCS 2017, this is the full version with all
appendice
Distributed computing building blocks for rational agents
Following [4] we extend and generalize the game-theoretic model of distributed computing, identifying different utility functions that encompass different potential preferences of players in a distributed system. A good distributed algorithm in the game-theoretic context is one that prohibits the agents (processors with interests) from de-viating from the protocol; any deviation would result in the agent losing, i.e., reducing its utility at the end of the algorithm. We dis-tinguish between different utility functions in the context of dis-tributed algorithms, e.g., utilities based on communication prefer-ence, solution preference, and output preference. Given these pref-erences we construct two basic building blocks for game theoretic distributed algorithms, a wake-up building block resilient to any preference and in particular to the communication preference (to which previous wake-up solutions were not resilient), and a knowl-edge sharing building block that is resilient to any and in partic-ular to solution and output preferences. Using the building blocks we present several new algorithms for consensus, and renaming as well as a modular presentation of the leader election algorithm of [4]