A Theoretical Model for Fork Analysis in the Bitcoin Network

Shahsavari, Yahya; Zhang, Kaiwen; Talhi, Chamseddine

doi:10.1109/blockchain.2019.00038

Cited by 47 publications

(27 citation statements)

References 14 publications

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“…λ i = λ c i , measured in a common scale λ. We are interested in the corresponding distribution for the minimum time, t min = min i {t i m } p 1 (t) = i=1 p i (t) N j =i C j (t) (10) where C i (t) are cumulative distribution functions for the mining times of different miners. which for the exponential distributions simply reads…”

Section: Resultsmentioning

confidence: 99%

“…the possibility of the decentralised services to keep working when the number of users increase. Recently, [10,11] showed in a series of simulations that the block propagation delay has a positive linear relationship with the block-size and that increasing the number of neighbours and the bandwidth can significantly speed the block propagation in the network. In [10] the authors also evaluate the probability of fork formation and the correspondences to the block size, average P2P bandwidth, and average number of neighbours per node respectively, based on the Erdős-Rényi random graph model.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Consensus formation on heterogeneous networks

Fadda¹,

He²,

Tessone³

et al. 2021

Preprint

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Reaching consensus -a macroscopic state where almost all the system constituents display the same microscopic state -is a necessity in multiple complex socio-technical and econo-technical systems: their correct functioning ultimately depends on it. In many distributed systems -of which blockchain-based applications are one example -the process of consensus formation is crucial not only for the emergence of a leading majority but for the very system to function. Inspired on this application, but with a broader applicability, we build a minimalistic network model of consensus formation for quantifying how central nodes -with respect to their average distance to others -can leverage on their position to obtain competitive advantage in the consensus process. We show that in a wide range of network topologies, the probability of forming a majority can significantly increase depending on the centrality of nodes that initiate the spreading. Further, we study the role that network topology plays on the consensus process: we show that central nodes in scale-free networks can win consensus in the network even if they broadcast states significantly later than peripheral ones.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Consensus formation on heterogeneous networks

Fadda¹,

He²,

Tessone³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the process of node interaction, the network latency mainly comes from the difficulty check of the sender and the hash verification of the block as well as the delay in the transmission of inv messages, getdata messages, and blocks or transactions between the sender and the receiver. In order to optimize the protocol of blocks and reduce the propagation delay, it was proposed in the literature [10][11][12] that singlenode optimization and pipelining of the propagation to reduce the propagation delay of blockchain networks, respectively. Among them, single-node optimization stipulates that the sender performs difficulty checking on the block, and the receiver performs hash verification on the block.…”

Section: Related Workmentioning

confidence: 99%

Blockchain Network Propagation Mechanism Based on P4P Architecture

Huang

Tan

Mao

et al. 2021

Security and Communication Networks

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Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain’s performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay.

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“…Most existing theories on the capacity and consistency of a network, such as the well-known Brewer's theorem [21] and PACELC theorem [22], are qualitative and cannot be readily applied to quantitatively evaluate the capacity of public blockchains. Some models have been designed to quantify the consistency and capacity of PoW-based public blockchains [2,18,19,20]. These models rely on specific assumptions that simplify [18,19,20] or decouple [2] the blockchain extension process across miners and thus compromise model accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Some studies employed stale block rates from simulations or empirical stale block rates to analyze blockchain security and performance [17,15]. The stale block rate was estimated under a bounded block propagation delay among miners and the assumption that a fork occurs if new blocks are mined before preceding blocks have propagated to all miners [18,19,20]. This assumption would sacrifice accuracy because the new blocks can be mined by the miners that have learned the latest blockchain.…”

Section: Introductionmentioning

confidence: 99%

Capacity Analysis of Public Blockchain

Wang,

Ni,

Zha

et al. 2021

Preprint

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As distributed ledgers, blockchains run consensus protocols which trade capacity for consistency, especially in non-ideal networks with incomplete connectivity and erroneous links. Existing studies on the tradeoff between capacity and consistency are only qualitative or rely on specific assumptions. This paper presents discrete-time Markov chain models to quantify the capacity of Proof-of-Work based public blockchains in non-ideal networks. The comprehensive model is collapsed to be ergodic under the eventual consistency of blockchains, achieving tractability and efficient evaluations of blockchain capacity. A closed-form expression for the capacity is derived in the case of two miners. Another important aspect is that we extend the ergodic model to analyze the capacity under strong consistency, evaluating the robustness of blockchains against double-spending attacks. Validated by simulations, the proposed models are accurate and reveal the effect of link quality and the distribution of mining rates on blockchain capacity and the ratio of stale blocks.

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A Theoretical Model for Fork Analysis in the Bitcoin Network

Cited by 47 publications

References 14 publications

Consensus formation on heterogeneous networks

Consensus formation on heterogeneous networks

Blockchain Network Propagation Mechanism Based on P4P Architecture

Capacity Analysis of Public Blockchain

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