Unobtrusive monitoring: Statistical dissemination latency estimation in Bitcoin's peer-to-peer network

Autor: David Mödinger, Franz J. Hauck, Jan-Hendrik Lorenz, Rens Wouter van der Heijden
Rok vydání: 2020
Předmět:
Computer science
Normal Distribution
Großbritannien
02 engineering and technology
Peer-to-peer
computer.software_genre
Geographical locations
Probability distribution
Time Measurement
0202 electrical engineering
electronic engineering
information engineering
Time measurements
Computer software
Computer Networks
Measurement
Multidisciplinary
System analysis
Applied Mathematics
Simulation and Modeling
Great Britain
Commerce
Software Engineering
Europe
Physical Sciences
Medicine
Engineering and Technology
020201 artificial intelligence & image processing
Network analysis
Normal distribution
Algorithms
Network Analysis
Computer network
Research Article
Gro��britannien
Cryptocurrency
Computer and Information Sciences
Science
Gaussian distribution
Netzwerkanalyse
Zeitmessung
Trust
Research and Analysis Methods
Peer Group
Computer Software
020204 information systems
ddc:330
Humans
European Union
Latency (engineering)
DDC 000 / Computer science
information & general works
Dissemination
Computer networks
business.industry
Wahrscheinlichkeitsverteilung
Bayes Theorem
Probability Theory
Probability Distribution
United Kingdom
DDC 330 / Economics
Algorithmus
ddc:000
People and places
business
Database transaction
computer
Mathematics
Zdroj: PLoS ONE
PLoS ONE, Vol 15, Iss 12, p e0243475 (2020)
ISSN: 1932-6203
Popis: The cryptocurrency system Bitcoin uses a peer-to-peer network to distribute new transactions to all participants. For risk estimation and usability aspects of Bitcoin applications, it is necessary to know the time required to disseminate a transaction within the network. Unfortunately, this time is not immediately obvious and hard to acquire. Measuring the dissemination latency requires many connections into the Bitcoin network, wasting network resources. Some third parties operate that way and publish large scale measurements. Relying on these measurements introduces a dependency and requires additional trust. This work describes how to unobtrusively acquire reliable estimates of the dissemination latencies for transactions without involving a third party. The dissemination latency is modelled with a lognormal distribution, and we estimate their parameters using a Bayesian model that can be updated dynamically. Our approach provides reliable estimates even when using only eight connections, the minimum connection number used by the default Bitcoin client. We provide an implementation of our approach as well as datasets for modelling and evaluation. Our approach, while slightly underestimating the latency distribution, is largely congruent with observed dissemination latencies.
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Databáze: OpenAIRE
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