quantitative analysis

Market Structure in Bitcoin Mining by June Ma, Joshua S. Gans, Rabee Tourky :: SSRN

Posted in algorithms, bitcoin, papers, quantitative analysis, Research Notes

Abstract

We analyze the Bitcoin protocol for electronic peer-to-peer payments and the operations that support the “blockchain” that underpins it. It is shown that the protocol maps formally into a dynamic game that is an extension of standard models of R&D racing. The model provides a technical foundation for any economic analysis of ‘proof-of-work’ protocols. Using the model, we demonstrate that free entry is solely responsible for determining resource usage by the system for a given reward to mining. The endogenous level of computational difficulty built into the Bitcoin protocol does not mitigate this usage and serves only to determine the time taken to process transactions. Regulating market structure will mitigate resource use highlighting the importance of identifying the benefits of competition for the operation of the blockchain.

Keywords: bitcoin, blockchain, racing, mining, competition, free entry

Source: Market Structure in Bitcoin Mining by June Ma, Joshua S. Gans, Rabee Tourky :: SSRN

Decentralization in Bitcoin and Ethereum

Posted in bitcoin, decentralization, quantitative analysis

We have been conducting a longitudinal study of the state of cryptocurrency networks, including Bitcoin and Ethereum. We have just made public our results from our study spanning 2015 to 2017, in a peer-reviewed paper about to be presented at the upcoming Financial Cryptography and Data Security conference in February [1].Here are some highlights from our findings.

Source: Decentralization in Bitcoin and Ethereum

Bitcoin Mining and Its Cost by Sailendra Prasanna Mishra :: SSRN

Posted in bitcoin, papers, quantitative analysis, Research Notes

Bitcoin Mining and Its Cost

9 Pages Posted: 5 Dec 2017

Sailendra Prasanna Mishra

University of Texas at Dallas – Naveen Jindal School of Management

Date Written: November 24, 2017

Abstract

As cryptocurrencies make a bid to become more mainstream, we investigate the resources needed to operationalize these at scales comparable to the traditional monetary system. As a first step, we study Bitcoin system, its mining process, and its resource requirements. Mining is a necessary process for the creation of bitcoins and verification of transactions. The process also makes the system decentralized and maintains security and trust in the system. Mining, which is tied to the solution of a complex crypto-puzzle, requires significant computing power. This resource demanding process has a significant impact on energy consumption. Our research looks at how the complexity of the problem to commit a transaction to the blockchain and the transaction volume impact computing capacity and energy consumption. We use data from January 2009 to October 2017 to analyze the problem. We highlight how the complexity, transaction volume, and price of bitcoins affect the mining capacity of miners. We find that the energy demand from mining activities will exceed 30,582 MW per month at 400 transactions/second. Our findings suggest that Bitcoin’s protocol induces competition among miners and computational power race, which may not be sustainable for future growth of the Bitcoin network.

Source: Bitcoin Mining and Its Cost by Sailendra Prasanna Mishra :: SSRN

Animation: Visualizing the ICO Explosion in One Timeline

Posted in quantitative analysis, Research Notes

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Animation: Visualizing the ICO ExplosionJEFF DESJARDINS on December 12, 2017 at 12:14 pmShareTweetShareRedditEmailSHARES 794Animation: Visualizing the ICO ExplosionIn our chart highlighting Bitcoin’s epic journey to $10,000, we also noted that 2017 was a landmark year for the Initial Coin Offering (ICO), a method used to raise initial funds for development and marketing of new cryptocurrencies or tokens.ICOs have become so popular that well over 90% of total funds raised through this mechanism came from this year alone.While it’s hard to put this sudden ICO explosion in context, we think today’s animation does the phenomenon sufficient justice. Coming from Max Galka at Elementus.io, today’s animation shows a timeline of ICOs and funds raised since early 2014.In an added dimension, each ICO is also classified based on geographic region. The colorful fireworks that happen throughout 2017 help to make it clear that we are indeed living in the year of the ICO.YEAR OF THE ICODespite bans in China and South Korea, there is no shortage of fervor for new cryptocurrencies or tokens.In their short history, there have been three ICOs that raised over $200 million – and six more that surpassed the $100 million mark.Here is a breakdown of the nine biggest ICOs so far. Note that eight of them took place in 2017:Name Location ICO Proceeds ICO YearFilecoin North America $257 million 2017Tezos Europe $236 million 2017EOS North America $200 million 2017Paragon North America $183 million 2017The DAO Stateless/Unknown $168 million 2016Bancor Middle East $153 million 2017Polkadot Europe $121 million 2017QASH Asia $112 million 2017Status Europe $109 million 2017It’s worth mentioning that with the price for bitcoins and ether both rising fast, that these ICOs have actually raised even more capital than initially shown. That’s because the dollar amounts above are based on the value of bitcoins and ether at the time of the raise.With billions in capital going into new projects, investors and speculators are anxiously waiting to see which coin or token will be the next Ethereum to take the market by storm.

Source: Animation: Visualizing the ICO Explosion in One Timeline

When the cookie meets the blockchain

Posted in bitcoin, identity, papers, quantitative analysis, Research NotesLeave a Comment on When the cookie meets the blockchain

Cryptocurrencies are portrayed as a more anonymous and less traceable method of payment than credit cards. So if you shop online and pay with Bitcoin or another cryptocurrency, how much privacy do you have? In a new paper, we show just how little.Websites including shopping sites typically have dozens of third-party trackers per site. These third parties track sensitive details of payment flows, such as the items you add to your shopping cart, and their prices, regardless of how you choose to pay. Crucially, we find that many shopping sites leak enough information about your purchase to trackers that they can link it uniquely to the payment transaction on the blockchain. From there, there are well-known ways to further link that transaction to the rest of your Bitcoin wallet addresses. You can protect yourself by using browser extensions such as Adblock Plus and uBlock Origin, and by using Bitcoin anonymity techniques like CoinJoin. These measures help, but we find that linkages are still possible.

Source: When the cookie meets the blockchain

BlockSci: a platform for blockchain science and exploration

Posted in quantitative analysis, related_projects, Research Notes, toolsLeave a Comment on BlockSci: a platform for blockchain science and exploration

The Bitcoin blockchain — currently 140GB and growing — contains a massive amount of data that can give us insights into the Bitcoin ecosystem, including how users, businesses, and miners operate. Today we’re announcing BlockSci, an open-source software tool that enables fast and expressive analysis of Bitcoin’s and many other blockchains, and an accompanying working paper that explains its design and applications. Our Jupyter notebook demonstrates some of BlockSci’s capabilities.Current tools for blockchain analysis depend on general-purpose databases that have full support for transactions. But that’s unnecessary for blockchain analysis where the data structures are append-only. We take advantage of this observation in the design of our custom in-memory blockchain database as well as an analysis library.BlockSci’s core infrastructure is written in C++ and optimized for speed. (For example, traversing every transaction input and output on the Bitcoin blockchain takes only 10.3 seconds on our r4.2xlarge EC2 machine.) To make analysis more convenient, we provide Python bindings and a Jupyter notebook interface. This interface is slower, but is ideal for exploratory analyses and allows users to quickly iterate when developing new queries.The code below shows the convenience of traversing the blockchain using straightforward Python idioms, built-in currency conversion using historical exchange-rate data, and the use of pandas DataFrames for analysis and visualization..fees = [sum(block.fees) for block in chain.range(‘2017’)]times = [block.time for block in chain.range(‘2017’)]converter = blocksci.CurrencyConverter()df = pandas.DataFrame({“Fee”:fees}, index=times)df = converter.satoshi_to_currency_df(df, chain)When plotted, it results in the following graph showing the average transaction fee per block:BlockSci uses a custom data format; it comes with a parser that generates this data from the serialized blockchain format recorded by cryptocurrency nodes such as bitcoind. The parser supports incremental updates when new blocks are received, and making it easy to stay up to date with the latest version of the blockchain. We’ve used BlockSci to analyze Bitcoin, Bitcoin Cash, Litecoin, Namecoin, Dash, and ZCash; many other cryptocurrencies make no changes to the blockchain format, and so should be supported with no changes to BlockSci.In our working paper, we present four analyses that show BlockSci’s usefulness for answering research questions. We show how multisignatures unfortunately weaken privacy and confidentiality; we apply the cluster intersection attack to Dash, a privacy-focused altcoin; we analyze inefficiencies in the usage of block space; and we present improved methods for estimating of how often coins change possession as opposed to just being shuffled around.Here’s an illustrative example. Exploratory graph analysis using BlockSci allowed us to discover a behavioral pattern in the usage of multisignatures that weakens security. Multisignatures are a security-enhancing mechanism that distribute control of an address over a number of different public keys. Surprisingly, we found that users often negate this security by moving their funds from a multisig address to a regular address and then back again after a period of a few hours to days. We think this happens when users are changing the access control policy on their wallet, although it is unclear why they transfer their funds to a regular address in the interim, and not directly to the new multisig address. This pattern of behavior has led over $12 million dollars to be left insecure over the course of  over 22,000 transactions. What users may not appreciate is that the temporary weakening of security is advertised to potential attackers on the blockchain.There’s far more to explore on public blockchains. BlockSci is publicly available now, and we hope you’ll find it useful. It is easy to get started using the EC2 image we’ve released, which includes the Bitcoin blockchain data in addition to the tool. BlockSci is open-source, and we welcome contributions. This is an alpha release; we’re continuing to improve it and the interface may change a bit in future releases. We look forward to working with the community and to hearing about other creative uses of the data and the to

Source: BlockSci: a platform for blockchain science and exploration

Bitcoin Energy Consumption Index – Digiconomist

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Key Network StatisticsDescription ValueBitcoin’s current estimated annual electricity consumption* (TWh) 16.36Annualized global mining revenues $3,643,528,563Annualized estimated global mining costs $818,077,659Country closest to Bitcoin in terms of electricity consumption CubaEstimated electricity used over the previous day (KWh) 44,826,173Implied Watts per GH/s 0.322Break-even Watts per GH/s (based on 5 cents per KWh) 1.434Electricity consumed per transaction (KWh) 174.00Number of U.S. households that could be powered by Bitcoin 1,514,959Number of U.S. households powered for 1 day by the electricity consumed for a single transaction 5.86Bitcoin’s electricity consumption as a percentage of the world’s electricity consumption 0.08%

Source: Bitcoin Energy Consumption Index – Digiconomist

Quantifying Decentralization – news.21.co

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The primary advantage of Bitcoin and Ethereum over their legacy alternatives is widely understood to be decentralization. However, despite the widely acknowledged importance of this property, most discussion on the topic lacks quantification. If we could agree upon a quantitative measure, it would allow us to:Measure the extent of a given system’s decentralizationDetermine how much a given system modification improves or reduces decentralizationDesign optimization algorithms and architectures to maximize decentralizationIn this post we propose the minimum Nakamoto coefficient as a simple, quantitative measure of a system’s decentralization, motivated by the well-known Gini coefficient and Lorenz curve.

Source: Quantifying Decentralization – news.21.co