2 research outputs found

    FairLedger: A Fair Blockchain Protocol for Financial Institutions

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    Financial institutions are currently looking into technologies for permissioned blockchains. A major effort in this direction is Hyperledger, an open source project hosted by the Linux Foundation and backed by a consortium of over a hundred companies. A key component in permissioned blockchain protocols is a byzantine fault tolerant (BFT) consensus engine that orders transactions. However, currently available BFT solutions in Hyperledger (as well as in the literature at large) are inadequate for financial settings; they are not designed to ensure fairness or to tolerate selfish behavior that arises when financial institutions strive to maximize their own profit. We present FairLedger, a permissioned blockchain BFT protocol, which is fair, designed to deal with rational behavior, and, no less important, easy to understand and implement. The secret sauce of our protocol is a new communication abstraction, called detectable all-to-all (DA2A), which allows us to detect participants (byzantine or rational) that deviate from the protocol, and punish them. We implement FairLedger in the Hyperledger open source project, using Iroha framework, one of the biggest projects therein. To evaluate FairLegder's performance, we also implement it in the PBFT framework and compare the two protocols. Our results show that in failure-free scenarios FairLedger achieves better throughput than both Iroha's implementation and PBFT in wide-area settings

    A Constructive Approach for Proving Data Structures’ Linearizability

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    Abstract. We present a comprehensive methodology for proving cor-rectness of concurrent data structures. We exemplify our methodology by using it to give a roadmap for proving linearizability of the popular Lazy List implementation of the concurrent set abstraction. Correctness is based on our key theorem, which captures sufficient conditions for lin-earizability. In contrast to prior work, our conditions are derived directly from the properties of the data structure in sequential runs, without requiring the linearization points to be explicitly identified.
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