Pipelined Algorithms to Detect Cheating in Long-Term Grid Computations

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.Comment: Expanded version with an additional figure; ISSN 0304-397

Fully-Dynamic Verifiable Zero-Knowledge Order Queries for Network Data

We show how to provide privacy-preserving (zero-knowledge) answers to order queries on network data that is organized in lists, trees, and partially-ordered sets of bounded dimension. Our methods are efficient and dynamic, in that they allow for updates in the ordering information while also providing for quick and verifiable answers to queries that reveal no information besides the answers to the queries themselves