DOCSLIB.ORG

Blockchain Consensus Algorithms: a Survey

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

1 Blockchain Consensus Algorithms: A Survey Md Sadek Ferdous, Member, IEEE, Mohammad Jabed Morshed Chowdhury, Mohammad A. Hoque, Member, IEEE, and Alan Colman F Abstract—In recent years, blockchain technology has received unpar- of Bitcoin [1] that was introduced in 2008. While crypto- alleled attention from academia, industry, and governments all around currencies have emerged as the principal and the most pop- the world. It is considered a technological breakthrough anticipated to ular application of blockchain technology, many enthusiasts disrupt several application domains touching all spheres of our lives. from different disciplines have identified and proposed a The sky-rocket anticipation of its potential has caused a wide-scale plethora of applications of blockchain in a multitude of exploration of its usage in different application domains. This has resul- ted in a plethora of blockchain systems for various purposes. However, application domains [2], [3]. The possibility of exploiting many of these blockchain systems suffer from serious shortcomings blockchain in so many areas has created huge anticipation related to their performance and security, which need to be addressed surrounding blockchain systems. Indeed, it is regarded as before any wide-scale adoption can be achieved. A crucial component one of the fundamental technologies to revolutionise the of any blockchain system is its underlying consensus algorithm, which landscapes of the identified application domains. in many ways, determines its performance and security. Therefore, to A blockchain system is, fundamentally, a distributed address the limitations of different blockchain systems, several existing system that relies on a consensus algorithm that ensures as well novel consensus algorithms have been introduced. A systematic agreement on the states of certain data among distributed analysis of these algorithms will help to understand how and why any nodes. A consensus algorithm is the core component that particular blockchain performs the way it functions. However, the existing studies of consensus algorithms are not comprehensive. Those studies directly dictates how such a system behaves and the per- have incomplete discussions on the properties of the algorithms and formance it can achieve. Distributed consensus has been fail to analyse several major blockchain consensus algorithms in terms a widely studied research topic in distributed systems, of their scopes. This article fills this gap by analysing a wide range of however, with the advent of blockchain, it has received consensus algorithms using a comprehensive taxonomy of properties renewed attention. A wide variety of crypto-currencies and by examining the implications of different issues still prevalent in targeting different application domains has introduced an consensus algorithms in detail. The result of the analysis is presented in array of unique requirements that can only be satisfied by tabular formats, which provides a visual illustration of these algorithms their corresponding consensus mechanisms. This fact has in a meaningful way. We have also analysed more than hundred top crypto-currencies belonging to different categories of consensus al- fuelled the need not only to examine the applicability of gorithms to understand their properties and to implicate different trends existing consensus algorithms in newer settings, but also to in these crypto-currencies. Finally, we have presented a decision tree innovate novel consensus algorithms. Consequently, several of algorithms to be used as a tool to test the suitability of consensus consensus algorithms have emerged, each of which pos- algorithms under different criteria. sesses interesting properties and unique capabilities. As the characteristics of various types of blockchain Index Terms—Blockchain, Distributed Consensus, Proof of Work, PoW, systems are fundamentally dependent on the consensus Proof of Stake, PoS, Delegated Proof of Stake, DPoS. algorithms they use, a systematic analysis of existing con- arXiv:2001.07091v2 [cs.DC] 7 Feb 2020 sensus algorithms is required. It is necessary to examine, compare, and contrast these algorithms. There have a been 1 INTRODUCTION a number of attempts aiming to fulfil this goal can be found in [4], [5], [6], [7], [8], [9], [10]. In particular, the works carried In the last few years, blockchain has received wide-spread out by Cachin et al. [4] and Bano et al. [5] are noteworthy as attention among the industry, the Government, and aca- they represent the pioneer works in this scope. Cachin et al., demia alike. This interest has been piqued by the success in their work, have explored different aspects of distributed systems and consensus and focused on consensus algorithm M. S. Ferdous is with Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh and Imperial College London, London SW7 2AZ, U.K. E- deployed in blockchain systems that are not to open to mail: [email protected]. the public. On the other hand, the focus of the work by Bano et al. is more general in the sense they have explored Mohammad Jabed Morshed Chowdhury is with La Trobe University, Mel- bourne, Victoria-3086, Australia. E-mail: [email protected]. consensus algorithms used both in public as well as private systems. Another exceptional work is by Wang et al. [6] in Mohammad A. Hoque is with University of Helsinki, 3835 Helsinki, Helsinki which the authors have presented a comprehensive survey Finland. E-mail: mohammad.a.hoque@helsinki.fi. of different aspects of consensus, mining, and blockchains Alan Coleman is with Swinburne University of Technology, Hawthorn, in a detailed fashion. Australia. E-mail: [email protected]. However, all these works have some major shortcom- 2 ings. For example, the factors upon which the consensus responding crypto-currencies. algorithms have been analysed are not comprehensive. Importantly, a wide range of consensus algorithms and their internal mechanisms utilised in many existing crypto- 2 BACKGROUND:DISTRIBUTED CONSENSUS currencies have not been considered at all. In addition, all Consensus mechanisms in distributed systems have been of these studies have failed to capture the practical interrela- a well studied research problem for nearly three decades. tion between blockchain systems (mostly crypto-currencies) Such mechanisms enable consensus to be achieved regard- and their corresponding consensus algorithms. All in all, ing a shared state/data among a set of distributed nodes. there is a pressing need for a study that analyses a wide The need for a shared state originated the notion of replic- range of existing consensus algorithms and the blockchain ated database systems in order to ensure resilience against systems in a practical-oriented way and synthesises this node failures within a network. Such database systems analyses into a conceptual framework in a concise yet com- ensure that data is not lost when one or more nodes fail prehensive manner. The principal motivation of this article to function in an excepted fashion. is to fill in this gap. The notion of the replicated database can be generalised with the concept of State Machine Replication (SMR) [11]. Contributions. The main contributions of the article are The core idea behind SMR is that a computing machine can presented below: be expressed as a deterministic state machine. The machine • A novel taxonomy of consensus properties, capturing accepts an input message, performs its predefined computa- different aspects of a consensus algorithm, has been cre- tion, and might produce an output/response. These actions ated. In this taxonomy, consensus algorithms have been essentially change its state. SMR conceptualises that such a categorised in two major categories: incentivised and state machine, with an initial state, can be replicated among non-incentivised algorithms, which have been again different nodes. If it can be ensured that all the participating sub-divided as per different considerations. Consensus nodes receive the same set of input messages in the exact algorithms belonging to each sub-category analysed same order (the phenomenon known as atomic broadcast), together using the taxonomy of consensus properties. then each node would be able to evolve the states of its • The analysis of each sub-category has been summarised state machine individually in exactly the same fashion. This in tabular formats so as to visually represent it in a can guarantee consistency and availability regarding the comprehensible way. state of the machine (as well as data it holds) among all • For each category (and the sub-category, if any), the cor- (applicable) nodes even in the presence of node failures. responding blockchain systems (predominantly crypto- Once this occurs, it can be said that a distributed consensus currencies) have been analysed as well. The ana- has emerged among the participating nodes. It is imperative lysis result has been presented in a concise fashion, that a protocol is defined to ensure timely dissemination which can be used to understand the inter-relation and atomic broadcast of input messages among the nodes between these systems and their underlying consensus and, in many ways, dictates how a distributed consensus is algorithms. achieved and maintained. Hence, such a protocol is aptly • The major issues in each category of consensus al- called a consensus protocol. gorithm have been examined in detail, and their im- Designing and deploying a consensus protocol is a chal- plications
Recommended publications
  • Realizing the Potential of Blockchain Technologies in Genomics

    Realizing the Potential of Blockchain Technologies in Genomics

    Downloaded from genome.cshlp.org on September 29, 2021 - Published by Cold Spring Harbor Laboratory Press Perspective Realizing the potential of blockchain technologies in genomics Halil Ibrahim Ozercan,1 Atalay Mert Ileri,2 Erman Ayday,1 and Can Alkan1 1Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey; 2Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA Genomics data introduce a substantial computational burden as well as data privacy and ownership issues. Data sets generated by high-throughput sequencing platforms require immense amounts of computational resources to align to reference ge- nomes and to call and annotate genomic variants. This problem is even more pronounced if reanalysis is needed for new ver- sions of reference genomes, which may impose high loads to existing computational infrastructures. Additionally, after the compute-intensive analyses are completed, the results are either kept in centralized repositories with access control, or dis- tributed among stakeholders using standard file transfer protocols. This imposes two main problems: (1) Centralized servers become gatekeepers of the data, essentially acting as an unnecessary mediator between the actual data owners and data users; and (2) servers may create single points of failure both in terms of service availability and data privacy. Therefore, there is a need for secure and decentralized platforms for data distribution with user-level data governance. A new technology, block- chain, may help ameliorate some of these problems. In broad terms, the blockchain technology enables decentralized, im- mutable, incorruptible public ledgers. In this Perspective, we aim to introduce current developments toward using blockchain to address several problems in omics, and to provide an outlook of possible future implications of the blockchain technology to life sciences.
  • YEUNG-DOCUMENT-2019.Pdf (478.1Kb)

    YEUNG-DOCUMENT-2019.Pdf (478.1Kb)

    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

    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.
  • GJR-GARCH Volatility Modeling Under NIG and ANN for Predicting Top Cryptocurrencies

    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.
  • Lab 5: Bittorrent Client Implementation

    Lab 5: Bittorrent Client Implementation

    Lab 5: BitTorrent Client Implementation Due: Nov. 30th at 11:59 PM Milestone: Nov. 19th during Lab Overview In this lab, you and your lab parterner will develop a basic BitTorrent client that can, at minimal, exchange a file between multiple peers, and at best, function as a full feature BitTorrent Client. The programming in this assignment will be in C requiring standard sockets, and thus you will not need to have root access. You should be able to develop your code on any CS lab machine or your personal machine, but all code will be tested on the CS lab. Deliverable Your submission should minimally include the following programs and files: • REDME • Makefile • bencode.c|h • bt lib.c|h • bt setup.c|h • bt client • client trace.[n].log • sample torrent.torrent Your README file should contain a short header containing your name, username, and the assignment title. The README should additionally contain a short description of your code, tasks accomplished, and how to compile, execute, and interpret the output of your programs. Your README should also contain a list of all submitted files and code and the functionality implemented in different code files. This is a large assignment, and this will greatly aid in grading. As always, if there are any short answer questions in this lab write-up, you should provide well marked answers in the README, as well as indicate that you’ve completed any of the extra credit (so that I don’t forget to grade it). Milestones The entire assignment will be submitted and graded, but your group will also hold a milestone meeting to receive feedback on your current implementation.
  • An Empirical Investigation of Volatility Dynamics in the Cryptocurrency Market

    An Empirical Investigation of Volatility Dynamics in the Cryptocurrency Market

    This is a repository copy of An empirical investigation of volatility dynamics in the cryptocurrency market. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/147457/ Version: Accepted Version Article: Katsiampa, P. orcid.org/0000-0003-0477-6503 (2019) An empirical investigation of volatility dynamics in the cryptocurrency market. Research in International Business and Finance. ISSN 0275-5319 https://doi.org/10.1016/j.ribaf.2019.06.004 Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). Reuse This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can’t change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ An empirical investigation of volatility dynamics in the cryptocurrency market Abstract: By employing an asymmetric Diagonal BEKK model, this paper examines volatility dynamics of five major cryptocurrencies, namely Bitcoin, Ether, Ripple, Litecoin, and Stellar Lumen. It is shown that the conditional variances of all the five cryptocurrencies are significantly affected by both previous squared errors and past conditional volatility.
  • OCC Interpretive Letter 1174: DLT and Stable Coins

    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)
  • Defending Against Malicious Reorgs in Tezos Proof-Of-Stake

    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.
  • Cryptocurrency: the Economics of Money and Selected Policy Issues

    Cryptocurrency: the Economics of Money and Selected Policy Issues

    Cryptocurrency: The Economics of Money and Selected Policy Issues Updated April 9, 2020 Congressional Research Service https://crsreports.congress.gov R45427 SUMMARY R45427 Cryptocurrency: The Economics of Money and April 9, 2020 Selected Policy Issues David W. Perkins Cryptocurrencies are digital money in electronic payment systems that generally do not require Specialist in government backing or the involvement of an intermediary, such as a bank. Instead, users of the Macroeconomic Policy system validate payments using certain protocols. Since the 2008 invention of the first cryptocurrency, Bitcoin, cryptocurrencies have proliferated. In recent years, they experienced a rapid increase and subsequent decrease in value. One estimate found that, as of March 2020, there were more than 5,100 different cryptocurrencies worth about $231 billion. Given this rapid growth and volatility, cryptocurrencies have drawn the attention of the public and policymakers. A particularly notable feature of cryptocurrencies is their potential to act as an alternative form of money. Historically, money has either had intrinsic value or derived value from government decree. Using money electronically generally has involved using the private ledgers and systems of at least one trusted intermediary. Cryptocurrencies, by contrast, generally employ user agreement, a network of users, and cryptographic protocols to achieve valid transfers of value. Cryptocurrency users typically use a pseudonymous address to identify each other and a passcode or private key to make changes to a public ledger in order to transfer value between accounts. Other computers in the network validate these transfers. Through this use of blockchain technology, cryptocurrency systems protect their public ledgers of accounts against manipulation, so that users can only send cryptocurrency to which they have access, thus allowing users to make valid transfers without a centralized, trusted intermediary.
  • A Decentralized Cloud Storage Network Framework

    A Decentralized Cloud Storage Network Framework

    Storj: A Decentralized Cloud Storage Network Framework Storj Labs, Inc. October 30, 2018 v3.0 https://github.com/storj/whitepaper 2 Copyright © 2018 Storj Labs, Inc. and Subsidiaries This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 license (CC BY-SA 3.0). All product names, logos, and brands used or cited in this document are property of their respective own- ers. All company, product, and service names used herein are for identification purposes only. Use of these names, logos, and brands does not imply endorsement. Contents 0.1 Abstract 6 0.2 Contributors 6 1 Introduction ...................................................7 2 Storj design constraints .......................................9 2.1 Security and privacy 9 2.2 Decentralization 9 2.3 Marketplace and economics 10 2.4 Amazon S3 compatibility 12 2.5 Durability, device failure, and churn 12 2.6 Latency 13 2.7 Bandwidth 14 2.8 Object size 15 2.9 Byzantine fault tolerance 15 2.10 Coordination avoidance 16 3 Framework ................................................... 18 3.1 Framework overview 18 3.2 Storage nodes 19 3.3 Peer-to-peer communication and discovery 19 3.4 Redundancy 19 3.5 Metadata 23 3.6 Encryption 24 3.7 Audits and reputation 25 3.8 Data repair 25 3.9 Payments 26 4 4 Concrete implementation .................................... 27 4.1 Definitions 27 4.2 Peer classes 30 4.3 Storage node 31 4.4 Node identity 32 4.5 Peer-to-peer communication 33 4.6 Node discovery 33 4.7 Redundancy 35 4.8 Structured file storage 36 4.9 Metadata 39 4.10 Satellite 41 4.11 Encryption 42 4.12 Authorization 43 4.13 Audits 44 4.14 Data repair 45 4.15 Storage node reputation 47 4.16 Payments 49 4.17 Bandwidth allocation 50 4.18 Satellite reputation 53 4.19 Garbage collection 53 4.20 Uplink 54 4.21 Quality control and branding 55 5 Walkthroughs ...............................................
  • In Re Bitconnect Securities Litigation 18-CV-80086-Third Amended Class Action Complaint

    In Re Bitconnect Securities Litigation 18-CV-80086-Third Amended Class Action Complaint

    Case 9:18-cv-80086-DMM Document 48 Entered on FLSD Docket 07/03/2018 Page 1 of 64 UNITED STATES DISTRICT COURT SOUTHERN DISTRICT OF FLORIDA WEST PALM BEACH DIVISION IN RE BITCONNECT SECURITIES LITIGATION Lead Case No.: 9:18-cv-80086-DMM CLASS ACTION JURY TRIAL DEMANDED THIRD AMENDED CLASS ACTION COMPLAINT Respectfully submitted, SILVER MILLER LEVI & KORSINSKY, LLP 11780 W. Sample Road EDUARD KORSINSKY Coral Springs, Florida 33065 E-mail: [email protected] Telephone: (954) 516-6000 30 Broad Street, 24th Floor DAVID C. SILVER New York, New York 10004 Florida Bar No. 572764 Telephone: (212) 363-7500 E-mail: [email protected] Facsimile: (212) 636-7171 JASON S. MILLER Florida Bar No. 072206 DONALD J. ENRIGHT E-mail: [email protected] E-mail: [email protected] ELIZABETH K. TRIPODI E-mail: [email protected] JOHN A. CARRIEL E-mail: [email protected] LEVI & KORSINSKY, LLP 1101 30th Street, N.W., Suite 115 Washington, DC 20007 Telephone: (202) 524-4290 Facsimile: (202) 333-2121 Co-Lead Counsel for Plaintiffs Case 9:18-cv-80086-DMM Document 48 Entered on FLSD Docket 07/03/2018 Page 2 of 64 Lead Case No.: 9:18-cv-80086-DMM TABLE OF CONTENTS Page NATURE OF THE ACTION ..........................................................................................................1 JURISDICTION AND VENUE ......................................................................................................8 PARTIES AND RELEVANT NON-PARTIES ..............................................................................8 I. PLAINTIFFS ...........................................................................................................8
  • A Cryptocurrency Spectrum Short Analysis

    A Cryptocurrency Spectrum Short Analysis

    Journal of Risk and Financial Management Review A Cryptocurrency Spectrum Short Analysis 1 2 3 Mircea Constantin S, cheau , Simona Liliana Crăciunescu , Iulia Brici and Monica Violeta Achim 3,* 1 Faculty of Automation, Computers and Electronics, University of Craiova, 200585 Craiova, Romania; [email protected] 2 Simona Liliana Crăciunescu, The Bucharest University of Economic Studies, 010374 Bucharest, Romania; [email protected] 3 Faculty of Economics and Business Administration, Babes, -Bolyai University, 400591 Cluj-Napoca, Romania; [email protected] * Correspondence: [email protected] Received: 30 June 2020; Accepted: 11 August 2020; Published: 17 August 2020 Abstract: Technological development brings about economic changes that affect most citizens, both in developed and undeveloped countries. The implementation of blockchain technologies that bring cryptocurrencies into the economy and everyday life also induce risks. Authorities are continuously concerned about ensuring balance, which is, among other things, a prudent attitude. Achieving this goal sometimes requires the development of standards and regulations applicable at the national or global level. This paper attempts to dive deeper into the worldwide operations, related to cryptocurrencies, as part of a general phenomenon, and also expose some of the intersections with cybercrime. Without impeding creativity, implementing suggested proposals must comply with the rules in effect and provide sufficient flexibility for adapting and integrating them. Different segments need to align or reposition, as alteration is only allowed in a positive way. Adopting cryptocurrency decisions should be unitary, based on standard policies. Keywords: cryptocurrencies; fraud; algorithms; correlations; impact; risks; regulation; blockchain 1. Introduction In the area of influence of computer science, the terms undergo rapid mutations, both in sense and interpretability.