Pipelined Algorithms to Detect Cheating in Long-Term Grid Computations

TL;DR

This paper introduces pipelined algorithms for detecting cheating in long-term distributed grid computations, effectively identifying colluding cheaters through interleaved task sequences and participant checks.

Contribution

It presents the first general-purpose cheater detection scheme that can catch colluding cheaters using pipelined algorithms and adaptations of parallel processor diagnosis solutions.

Findings

The algorithms can detect all cheaters, including colluders.

They tolerate collusions of lazy cheaters proportional to total participants.

Economic analysis suggests optimal deterrence parameters.

Abstract

This paper studies pipelined algorithms for protecting distributed grid computations from cheating participants, who wish to be rewarded for tasks they receive but don't perform. We present improved cheater detection algorithms that utilize natural delays that exist in long-term grid computations. In particular, we partition the sequence of grid tasks into two interleaved sequences of task rounds, and we show how to use those rounds to devise the first general-purpose scheme that can catch all cheaters, even when cheaters collude. The main idea of this algorithm might at first seem counter-intuitive--we have the participants check each other's work. A naive implementation of this approach would, of course, be susceptible to collusion attacks, but we show that by, adapting efficient solutions to the parallel processor diagnosis problem, we can tolerate collusions of lazy cheaters, even if the number of such cheaters is a fraction of the total number of participants. We also include a simple economic analysis of cheaters in grid computations and a parameterization of the main deterrent that can be used against them--the probability of being caught.