Distributed Causal Memory in the Presence of Byzantine Servers

Tseng, Lewis; Wang, Zezhi; Zhao, Yang; Pan, Haochen

doi:10.1109/nca.2019.8935059

Cited by 10 publications

(10 citation statements)

References 30 publications

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“…In the aforementioned algorithm proposed in [39], an initial broadcast was fulfilled by non-faulty agents. In [40], through asynchronous message-passing networks which may include the issue of byzantine failure, distributed causal shared memory in the client-server model was considered. The issue of lattice agreement was investigated in asynchronous systems in [41] where each process would make a decision over an metric from a per-defined lattice under the assumption of the problem of byzantine failure.…”

Section: ) Byzantine Attacks In Crnsmentioning

confidence: 99%

Adversarial Robustness in Cognitive Radio Networks

Zamanipour¹

2022

Preprint

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When an adversary gets access to the data sample in the adversarial robustness models and can make data-dependent changes, how has the decision maker consequently, relying deeply upon the adversarially-modified data, to make statistical inference? How can the resilience and elasticity of the network be literally justified from a game theoretical viewpoint − if there exists a tool to measure the aforementioned elasticity? The principle of byzantine resilience distributed hypothesis testing (BRDHT) is considered in this paper for cognitive radio networks (CRNs) − withoutloss-of-generality, something that can be extended to any type of homogeneous or heterogeneous networks. We use the temporal rate of the 𝛼−leakage as the appropriate tool which we measure the aforementioned resilience through. We take into account the main problem from an information theoretic point of view via an exploration over the adversarial robustness of distributed hypothesis testing rules. We chiefly examine if one can write F = 𝑚𝑎 for the main problem, consequently, we define a nested bilevel − even 3-level including a hidden control-law − mean-fieldgame (MFG) realisation solving the control dynamics as well. Further discussions are also provided e.g. the synchronisation. Our novel online algorithm − which is named OBRDHT − and solution are both unique and generic over which an evaluation is finally performed by simulations.

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Section: ) Byzantine Attacks In Crnsmentioning

confidence: 99%

Adversarial Robustness in Cognitive Radio Networks

Zamanipour¹

2022

Preprint

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“…We show that a matrix clock based data structure is required for ordering point-to-point messages and therefore the broadcast technique will not be applicable to a point-to-point setting. There has been recent interest in applying the Byzantine fault model in implementing causal consistency in distributed shared memory and replicated databases [7], [5], [6]. In [6], Byzantine causal broadcast has been used to implement Byzantine eventual consistency.…”

Section: Previous Workmentioning

confidence: 99%

“…In [6], Byzantine causal broadcast has been used to implement Byzantine eventual consistency. In [7], Byzantine reliable broadcast [15] is used to remove misinformation. induced by the combination of asynchrony and Byzantine behaviour.…”

Section: Previous Workmentioning

confidence: 99%

“…Most of the work on causal ordering of messages did not consider the Byzantine failure model. The only work on causal ordering under the Byzantine failure model was the recent result by Auvolat et al [4] which considered only Byzantinetolerant causal broadcasts, and the work in [5], [6], [7] which relied on broadcasts. To the best of our knowledge, there has been no work on Byzantine-tolerant causal ordering of pointto-point messages.…”

Section: Introductionmentioning

confidence: 99%

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Byzantine Fault Tolerant Causal Ordering

Misra¹,

Kshemkalyani²

2021

Preprint

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Causal ordering in an asynchronous system has many applications in distributed computing, including in replicated databases and real-time collaborative software. Previous work in the area focused on ordering point-to-point messages in a fault-free setting, and on ordering broadcasts under various fault models. To the best of our knowledge, Byzantine faulttolerant causal ordering has not been attempted for point-topoint communication in an asynchronous setting. In this paper, we first show that existing algorithms for causal ordering of point-to-point communication fail under Byzantine faults. We then prove that it is impossible to causally order messages under point-to-point communication in an asynchronous system with one or more Byzantine failures. We then present two algorithms that can causally order messages under Byzantine failures, where the network provides an upper bound on the message transmission time. The proofs of correctness for these algorithms show that it is possible to achieve causal ordering for point-to-point communication under a stronger asynchrony model where the network provides an upper bound on message transmission time. We also give extensions of our two algorithms for Byzantine fault-tolerant causal ordering of multicasts.

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“…There are plenty of research prototypes and industrial deployments of causally consistent distributed systems (e.g., COPS [16], Eiger [17], GentleRain [9], Cure [2], MongoDB [20], and Byz-RCM [19]). GentleRain uses a stabilization mechanism to make updates visible while respecting causal consistency.…”

Section: Introductionmentioning

confidence: 99%

Byz-GentleRain: An Efficient Byzantine-tolerant Causal Consistency Protocol

Huang¹,

Wei²,

Huang³

et al. 2021

Preprint

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Causal consistency is a widely used weak consistency model that allows high availability despite network partitions. There are plenty of research prototypes and industrial deployments of causally consistent distributed systems. However, as far as we know, none of them consider Byzantine faults, except Byz-RCM proposed by Tseng et al. Byz-RCM achieves causal consistency in the client-server model with 3f + 1 servers where up to f servers may suffer Byzantine faults, but assumes that clients are non-Byzantine. In this work, we present Byz-Gentlerain, the first causal consistency protocol which tolerates up to f Byzantine servers among 3f + 1 servers in each partition and any number of Byzantine clients. Byz-GentleRain is inspired by the stabilization mechanism of GentleRain for causal consistency. To prevent causal violations due to Byzantine faults, Byz-GentleRain relies on PBFT to reach agreement on a sequence of global stable times and updates among servers, and only updates with timestamps less than or equal to such common global stable times are visible to clients. We prove that Byz-GentleRain achieves Byz-CC, the causal consistency variant in the presence of Byzantine faults. We evaluate Byz-GentleRain on Aliyun. The preliminary results show that Byz-GentleRain is efficient on typical workloads.

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Distributed Causal Memory in the Presence of Byzantine Servers

Cited by 10 publications

References 30 publications

Adversarial Robustness in Cognitive Radio Networks

Adversarial Robustness in Cognitive Radio Networks

Byzantine Fault Tolerant Causal Ordering

Byz-GentleRain: An Efficient Byzantine-tolerant Causal Consistency Protocol

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