A Survey on Blockchain Consensus with a Performance Comparison of Pow, Pos and Pure Pos
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Useful Computation on the Block Chain The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Yeung, Fuk. 2019. Useful Computation on the Block Chain. Master's thesis, Harvard Extension School. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364565 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA 111 Useful Computation on the Block Chain Fuk Yeung A Thesis in the Field of Information Technology for the Degree of Master of Liberal Arts in Extension Studies Harvard University November 2019 Copyright 2019 [Fuk Yeung] Abstract The recent growth of blockchain technology and its usage has increased the size of cryptocurrency networks. However, this increase has come at the cost of high energy consumption due to the processing power needed to maintain large cryptocurrency networks. In the largest networks, this processing power is attributed to wasted computations centered around solving a Proof of Work algorithm. There have been several attempts to address this problem and it is an area of continuing improvement. We will present a summary of proposed solutions as well as an in-depth look at a promising alternative algorithm known as Proof of Useful Work. This solution will redirect wasted computation towards useful work. We will show that this is a viable alternative to Proof of Work. Dedication Thank you to everyone who has supported me throughout the process of writing this piece. -
Blockchain in an Internet-Of-Things Network Based on User Participation
Blockchain in an Internet-of-Things Network Based on User Participation Robert Ljungblad Computer Science and Engineering, bachelor's level 2019 Luleå University of Technology Department of Computer Science, Electrical and Space Engineering ABSTRACT The internet-of-things is the relatively new and rapidly growing concept of connecting everyday devices to the internet. Every day more and more devices are added to the internet-of-things and it is not showing any signs of slowing down. In addition, advancements in new technologies such as blockchains, artificial intelligence, virtual reality and machine learning are made practically every day. However, there are still much to learn about these technologies. This thesis explores the possibilities of blockchain technology by applying it to an internet-of-things network based on user participation. More specifically, it is applied to a use case derived from Luleå Kommun’s wishes to easier keep track of how full the city’s trash cans are. The goal of the thesis is to learn more about how blockchains can help an internet-of-things network as well as what issues can arise. The method takes an exploratory approach to the problem by partaking in a workshop with Luleå Kommun and by performing a literature study. It also takes a qualitative approach by creating a proof-of-concept solution to experience the technology firsthand. The final proof-of-concept as well as issues that arose during the project are analysed with the help of information gathered and experience gained throughout the project. It is concluded that blockchain technology can help communication in an internet-of-things network based on user participation. -
Casper the Friendly Finality Gadget
Casper the Friendly Finality Gadget Vitalik Buterin and Virgil Griffith Ethereum Foundation Abstract We introduce Casper, a proof of stake-based finality system which overlays an existing proof of work blockchain. Casper is a partial consensus mechanism combining proof of stake algorithm research and Byzantine fault tolerant consensus theory. We introduce our system, prove some desirable features, and show defenses against long range revisions and catastrophic crashes. The Casper overlay provides almost any proof of work chain with additional protections against block reversions. 1. Introduction Over the past few years there has been considerable research into “proof of stake” (PoS) based blockchain consensus algorithms. In a PoS system, a blockchain appends and agrees on new blocks through a process where anyone who holds coins inside of the system can participate, and the influence an agent has is proportional to the number of coins (or “stake”) it holds. This is a vastly more efficient alternative to proof of work (PoW) “mining” and enables blockchains to operate without mining’s high hardware and electricity costs. There are two major schools of thought in PoS design. The first, chain-based proof of stake[1, 2], mimics proof of work mechanics and features a chain of blocks and simulates mining by pseudorandomly assigning the right to create new blocks to stakeholders. This includes Peercoin[3], Blackcoin[4], and Iddo Bentov’s work[5]. The other school, Byzantine fault tolerant (BFT) based proof of stake, is based on a thirty-year-old body of research into BFT consensus algorithms such as PBFT[6]. BFT algorithms typically have proven mathematical 2 properties; for example, one can usually mathematically prove that as long as > 3 of protocol participants are following the protocol honestly, then, regardless of network latency, the algorithm cannot finalize conflicting blocks. -
Beauty Is Not in the Eye of the Beholder
Insight Consumer and Wealth Management Digital Assets: Beauty Is Not in the Eye of the Beholder Parsing the Beauty from the Beast. Investment Strategy Group | June 2021 Sharmin Mossavar-Rahmani Chief Investment Officer Investment Strategy Group Goldman Sachs The co-authors give special thanks to: Farshid Asl Managing Director Matheus Dibo Shahz Khatri Vice President Vice President Brett Nelson Managing Director Michael Murdoch Vice President Jakub Duda Shep Moore-Berg Harm Zebregs Vice President Vice President Vice President Shivani Gupta Analyst Oussama Fatri Yousra Zerouali Vice President Analyst ISG material represents the views of ISG in Consumer and Wealth Management (“CWM”) of GS. It is not financial research or a product of GS Global Investment Research (“GIR”) and may vary significantly from those expressed by individual portfolio management teams within CWM, or other groups at Goldman Sachs. 2021 INSIGHT Dear Clients, There has been enormous change in the world of cryptocurrencies and blockchain technology since we first wrote about it in 2017. The number of cryptocurrencies has increased from about 2,000, with a market capitalization of over $200 billion in late 2017, to over 8,000, with a market capitalization of about $1.6 trillion. For context, the market capitalization of global equities is about $110 trillion, that of the S&P 500 stocks is $35 trillion and that of US Treasuries is $22 trillion. Reported trading volume in cryptocurrencies, as represented by the two largest cryptocurrencies by market capitalization, has increased sixfold, from an estimated $6.8 billion per day in late 2017 to $48.6 billion per day in May 2021.1 This data is based on what is called “clean data” from Coin Metrics; the total reported trading volume is significantly higher, but much of it is artificially inflated.2,3 For context, trading volume on US equity exchanges doubled over the same period. -
Validation of Smart Contracts Through Automated Tooling
Faculty of Sciences and Tecnology Department of Informatics Engineering Validation of Smart Contracts Through Automated Tooling Tom´asMorgado de Carvalho Concei¸c~ao Internship Report in the context of the Master in Informatics Engineering, Specialization in Software Engineering advised by Professor Raul Barbosa and Eng. Jo~aoBarbosa and presented to the Department of Informatics Engineering / Faculty of Sciences and Technology January 2019 Acknowledgements I would first like to thank Professor Raul Barbosa, my advisor at the University of Coimbra, for, with his valuable experience and insights, guiding me through this process and always challenging me to do better. I would like to thank my advisor, Jo~aoBarbosa, not only for his technical contributions to this dissertation but for always ensuring that I had my priorities straight and was managing my time properly. Thank you for pushing me to always make healthy choices regarding my work-life balance. I want to thank the members of my jury, Professor Fernando Jos´eBarros and Professor C´esarAlexandre Domingues Teixeira, for their input and suggestions for improvement, and their constructive criticism. I would like to thank Whitesmith and Blocksmith, and in particular Gon¸caloLouzada, Maria Jo~aoFerreira and Rafael Jegundo, for accepting me on this internship, providing me with all the tools and conditions to achieve my goals and for inviting me to take part on other projects during this year. I would also like to thank my co-workers, who, since the first day and without exception, made me feel welcome and at home. I have learned something new with every single one of you. -
Evmpatch: Timely and Automated Patching of Ethereum Smart Contracts
EVMPatch: Timely and Automated Patching of Ethereum Smart Contracts Michael Rodler Wenting Li Ghassan O. Karame University of Duisburg-Essen NEC Laboratories Europe NEC Laboratories Europe Lucas Davi University of Duisburg-Essen Abstract some of these contracts hold, smart contracts have become an appealing target for attacks. Programming errors in smart Recent attacks exploiting errors in smart contract code had contract code can have devastating consequences as an attacker devastating consequences thereby questioning the benefits of can exploit these bugs to steal cryptocurrency or tokens. this technology. It is currently highly challenging to fix er- rors and deploy a patched contract in time. Instant patching is Recently, the blockchain community has witnessed several especially important since smart contracts are always online incidents due smart contract errors [7, 39]. One especially due to the distributed nature of blockchain systems. They also infamous incident is the “TheDAO” reentrancy attack, which manage considerable amounts of assets, which are at risk and resulted in a loss of over 50 million US Dollars worth of often beyond recovery after an attack. Existing solutions to Ether [31]. This led to a highly debated hard-fork of the upgrade smart contracts depend on manual and error-prone pro- Ethereum blockchain. Several proposals demonstrated how to defend against reentrancy vulnerabilities either by means of cesses. This paper presents a framework, called EVMPATCH, to instantly and automatically patch faulty smart contracts. offline analysis at development time or by performing run-time validation [16, 23, 32, 42]. Another infamous incident is the EVMPATCH features a bytecode rewriting engine for the pop- ular Ethereum blockchain, and transparently/automatically parity wallet attack [39]. -
GJR-GARCH Volatility Modeling Under NIG and ANN for Predicting Top Cryptocurrencies
Journal of Risk and Financial Management Article GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies Fahad Mostafa 1,* , Pritam Saha 2, Mohammad Rafiqul Islam 3 and Nguyet Nguyen 4,* 1 Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA 2 Rawls College of Business, Texas Tech University, Lubbock, TX 79409, USA; [email protected] 3 Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA; [email protected] 4 Department of Mathematics and Statistics, Youngstown State University, Youngstown, OH 44555, USA * Correspondence: [email protected] (F.M.); [email protected] (N.N.) Abstract: Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models. Citation: Mostafa, Fahad, Pritam Saha, Mohammad Rafiqul Islam, and Keywords: artificial neural network; cryptocurrency; GJR-GARCH; NIG; Monte Carlo simulation; Nguyet Nguyen. -
Decentralized Autonomous Organization Supplement."
ARTICLE 1 - PROVISIONS 17-31-101. Short title. This chapter shall be known and may be cited as the "Wyoming Decentralized Autonomous Organization Supplement." 17-31-102. Definitions. (a) As used in this chapter: (i) "Blockchain" means as defined in W.S. 34-29- 106(g)(i); (ii) "Decentralized autonomous organization" means a limited liability company organized under this chapter; (iii) "Digital asset" means as defined in W.S. 34-29- 101(a)(i); (iv) "Limited liability autonomous organization" or "LAO" means a decentralized autonomous organization; (v) "Majority of the members," means the approval of more than fifty percent (50%) of participating membership interests in a vote for which a quorum of members is participating. A person dissociated as a member as set forth in W.S. 17-29-602 shall not be included for the purposes of calculating the majority of the members; (vi) "Membership interest" means a member's ownership share in a member managed decentralized autonomous organization, which may be defined in the entity's articles of organization, smart contract or operating agreement. A membership interest may also be characterized as either a digital security or a digital consumer asset as defined in W.S. 34-29-101, if designated as such in the organization's articles of organization or operating agreement; (vii) "Open blockchain" means a blockchain as defined in W.S. 34-29-106(g)(i) that is publicly accessible and its ledger of transactions is transparent; (viii) "Quorum" means a minimum requirement on the sum of membership interests participating in a vote for that vote to be valid; 1 (ix) "Smart contract" means an automated transaction, as defined in W.S. -
OCC Interpretive Letter 1174: DLT and Stable Coins
OCC interpretive letter 1174: DLT and stable coins Author: Ousmène Jacques Mandeng, Senior Advisor, Blockchain and Multiparty Systems, Accenture Table of Contents OCC interpretive letter 1174: DLT and stable coins .................................................. 2 OCC’s position on DLT and stable coins ................................................................. 2 DLT-platforms .......................................................................................................... 3 Tokens ..................................................................................................................... 4 Stable coins ............................................................................................................. 4 Token payments ...................................................................................................... 5 Token networks ....................................................................................................... 5 Bank balance sheet tokenization ............................................................................ 6 Next steps ................................................................................................................. 7 Copyright © 2021 Accenture. All rights reserved. 1 United States OCC interpretive letter 1174: DLT and stable coins The United States Office of the Comptroller of the Currency (OCC), a federal supervisor of national banks and cooperative banks, issued new general guidance about stable coins and distributed ledger technology (DLT) -
State of the Art: the Monero Cryptocurrency Mining Malware Detection Using Supervised Machine Learning Algorithms
International Journal of Applied Science and Engineering State of the art: The monero cryptocurrency mining malware detection using supervised machine learning algorithms Wilfridus Bambang Triadi Handaya1*, Mohd Najwadi Yusoff 2, Aman Jantan2 1 Department of Informatics, Universitas Atma Jaya Yogyakarta, Daerah Istimewa Yogyakarta, Indonesia 2 School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia ABSTRACT Today’s evolving information technology trend is fuelling increasingly various cybercrime. Various cybercrime types, such as malware attacks and data breach activities, organizations, and countries, occur every day. Malware is one of the biggest cyber threats on the Internet today. Every year, the number of data breaches continues to increase. Mining is a complete cryptocurrency network’s computing process to verify transaction records, called blockchains, and receive digital coins in return. This mining process requires severe hardware and significant CPU resources to create cryptocurrencies. A statistic published by Statista in mid-2020 about the most detected crypto-mining malware types influencing corporate networks global from January to June 2020, it can be seen that cryptocurrency mining activity leading to the pool of Monero amounts to 46% derived from the use of XMRig. Malware detection is like an endless war between malware authors and malware prevention vendors. This trend change also makes the procedures and forms of analysis must adapt. From previously done manually with various tools for static analysis, to be OPEN ACCESS subsequently replaced with automatic analysis through the application of machine learning algorithms. Received: October 9, 2020 Revised: December 20, 2020 Keywords: Malware detection, XMRig, Monero, Cryptocurrencies, Mining. Accepted: January 6, 2021 Corresponding Author: Wilfridus Bambang Triadi Handaya 1. -
Defending Against Malicious Reorgs in Tezos Proof-Of-Stake
Defending Against Malicious Reorgs in Tezos Proof-of-Stake Michael Neuder Daniel J. Moroz Harvard University Harvard University [email protected] [email protected] Rithvik Rao David C. Parkes Harvard University Harvard University [email protected] [email protected] ABSTRACT Conference on Advances in Financial Technologies (AFT ’20), October 21– Blockchains are intended to be immutable, so an attacker who is 23, 2020, New York, NY, USA. ACM, New York, NY, USA , 13 pages. https: //doi.org/10.1145/3419614.3423265 able to delete transactions through a chain reorganization (a ma- licious reorg) can perform a profitable double-spend attack. We study the rate at which an attacker can execute reorgs in the Tezos 1 INTRODUCTION Proof-of-Stake protocol. As an example, an attacker with 40% of the Blockchains are designed to be immutable in order to protect against staking power is able to execute a 20-block malicious reorg at an attackers who seek to delete transactions through chain reorga- average rate of once per day, and the attack probability increases nizations (malicious reorgs). Any attacker who causes a reorg of super-linearly as the staking power grows beyond 40%. Moreover, the chain could double-spend transactions, meaning they commit an attacker of the Tezos protocol knows in advance when an at- a transaction to the chain, receive some goods in exchange, and tack opportunity will arise, and can use this knowledge to arrange then delete the transaction, effectively robbing their counterparty. transactions to double-spend. We show that in particular cases, the Nakamoto [15] demonstrated that, in a Proof-of-Work (PoW) setting, Tezos protocol can be adjusted to protect against deep reorgs. -
Plantuml Language Reference Guide (Version 1.2021.2)
Drawing UML with PlantUML PlantUML Language Reference Guide (Version 1.2021.2) PlantUML is a component that allows to quickly write : • Sequence diagram • Usecase diagram • Class diagram • Object diagram • Activity diagram • Component diagram • Deployment diagram • State diagram • Timing diagram The following non-UML diagrams are also supported: • JSON Data • YAML Data • Network diagram (nwdiag) • Wireframe graphical interface • Archimate diagram • Specification and Description Language (SDL) • Ditaa diagram • Gantt diagram • MindMap diagram • Work Breakdown Structure diagram • Mathematic with AsciiMath or JLaTeXMath notation • Entity Relationship diagram Diagrams are defined using a simple and intuitive language. 1 SEQUENCE DIAGRAM 1 Sequence Diagram 1.1 Basic examples The sequence -> is used to draw a message between two participants. Participants do not have to be explicitly declared. To have a dotted arrow, you use --> It is also possible to use <- and <--. That does not change the drawing, but may improve readability. Note that this is only true for sequence diagrams, rules are different for the other diagrams. @startuml Alice -> Bob: Authentication Request Bob --> Alice: Authentication Response Alice -> Bob: Another authentication Request Alice <-- Bob: Another authentication Response @enduml 1.2 Declaring participant If the keyword participant is used to declare a participant, more control on that participant is possible. The order of declaration will be the (default) order of display. Using these other keywords to declare participants