Xiang Fu, Wang Huaimin, Shi Peichang, Yingwei Fu · 5 authors
With the development of Economic Globalization, traditional single-cloud providers can not meet the needs of the explosive, global, diverse cloud services. JointCloud aims at empowering the cooperation among multiple Cloud Service Providers (CSP) to provide cross-cloud services. Our work in this paper is mainly focused on the accounting technology for JointCloud computing and we propose the JCLedger - a blockchain based distributed ledger. A new participant CCP (Cryptocurrency Provider) is introduced into the JointCloud collaboration environment to provide the cryptocurrency transferred. We have a detailed description of JCLedger model. We further analyze the four most important mechanisms for JCLedger and provide basic perspectives for in-depth analysis. Finally, we discuss the innovations of JCLedger and our future work in this field.
Giuseppe Pappalardo, Tiziana Di Matteo, Guido Caldarelli, Tomaso Aste
We investigate Bitcoin network observing transactions broadcasted into the network during a week from 04/05/2016 and then monitoring their inclusion into the blockchain during the following seven months.We unveil that 42% of the transactions are still not included in the Blockchain after 1 h from their appearance and 20% of the transactions are still not included in the Blockchain after 30 days, therefore revealing a great inefficiency in the Bitcoin system. However, we observe that most of these “forgotten” transactions have low values and in terms of transferred value the system is less inefficient with 93% of the transactions value being included into the Blockchain within 3 h and 98.8% within a day. The fact that a sizeable fraction of transactions is not processed timely casts serious doubts on the usability of the Bitcoin Blockchain for reliable time-stamping purposes. It also calls for a debate about the right systems of incentives which a peer-to-peer unintermediated system should introduce to promote efficient transaction recording
Open access
3 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Spyros Makridakis, Antonis Polemitis, George M. Giaglis, Soula Louca
Blockchain technologies, once used exclusively for buying and selling bitcoins, have entered the mainstream of computer applications, fundamentally changing the way Internet transactions can be implemented by ascertaining trust between unknown parties. In addition, they ensure immutability (once information is entered it cannot be modified) and enable disintermediation (as trust is assured, no third party is required to verify transactions). These advantages can produce disruptive changes when properly exploited, inspiring a large number of applications. These applications are forming the backbone of what can be called the Internet of Value, bound to bring as significant changes as those brought over the last 20 years by the traditional Internet. This chapter investigates blockchain and the technologies behind it and explains their technological might and outstanding potential, not only for transactions but also as distributed databases. It also discusses its future prospects and the disruptive changes it promises to bring, while also considering the challenges that would need to be overcome for its widespread adoption. Finally, the chapter considers combining blockchain with Artificial Intelligence (AI) and discusses the revolutionary changes that would result by rapidly advancing the AI field.
Massimo Bartoletti, Stefano Lande, Livio Pompianu, Andrea Bracciali
Modern cryptocurrencies exploit decentralised blockchains to record a public and unalterable history of transactions. Besides transactions, further information is stored for different, and often undisclosed, purposes, making the blockchains a rich and increasingly growing source of valuable information, in part of difficult interpretation. Many data analytics have been developed, mostly based on specifically designed and ad-hoc engineered approaches. We propose a general-purpose framework, seamlessly supporting data analytics on both Bitcoin and Ethereum --- currently the two most prominent cryptocurrencies. Such a framework allows us to integrate relevant blockchain data with data from other sources, and to organise them in a database, either SQL or NoSQL. Our framework is released as an open-source Scala library. We illustrate the distinguishing features of our approach on a set of significant use cases, which allow us to empirically compare ours to other competing proposals, and evaluate the impact of the database choice on scalability.
The problem of anomaly detection has been studied for a long time, and many Network Analysis techniques have been proposed as solutions. Although some results appear to be quite promising, no method is clearly to be superior to the rest. In this paper, we particularly consider anomaly detection in the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use the laws of power degree and densification and local outlier factor (LOF) method (which is proceeded by k-means clustering method) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes. We remark that the methods used here can be applied to any type of setting with an inherent graph structure, including, but not limited to, computer networks, telecommunications networks, auction networks, security networks, social networks, Web networks, or any financial networks. We use the Bitcoin transaction network in this paper due to the availability, size, and attractiveness of the data set.
Cryptocurrencies that are based on Proof-of-Work (PoW) often rely on special purpose hardware to perform so-called mining operations that secure the system, with miners receiving freshly minted tokens as a reward for their work. A notable example of such a cryptocurrency is Bitcoin, which is primarily mined using application specific integrated circuit (ASIC) based machines. Due to the supposed profitability of cryptocurrency mining, such hardware has been in great demand in recent years, in-spite of high associated costs like electricity. In this work, we show that because mining rewards are given in the mined cryptocurrency, while expenses are usually paid in some fiat currency such as the United States Dollar (USD), cryptocurrency mining is in fact a bundle of financial options. When exercised, each option converts electricity to tokens. We provide a method of pricing mining hardware based on this insight, and prove that any other price creates arbitrage. Our method shows that contrary to the popular belief that mining hardware is worth less if the cryptocurrency is highly volatile, the opposite effect is true: volatility increases value. Thus, if a coin's volatility decreases, some miners may leave, affecting security. We compare the prices produced by our method to prices obtained from popular tools currently used by miners and show that the latter only consider the expected returns from mining, while neglecting to account for the inherent risk in mining, which is due to the high exchange-rate volatility of cryptocurrencies. Finally, we show that the returns made from mining can be imitated by trading in bonds and coins, and create such imitating investment portfolios. Historically, realized revenues of these portfolios have outperformed mining, showing that indeed hardware is mispriced.
Marco Stocchi, Maria Ilaria Lunesu, Simona Ibba, Gavina Baralla · 5 authors
In recent years search engines have become the go-to methods for achieving many types of knowledge, spanning from detailed descriptions or general information interesting to the user. Likewise several reassignment techniques are capturing the attention of researchers in the field of signal analysis. Particularly, the Synchrosqueezing Wavelet Transform - SST allows signal decomposition and instantaneous frequency extrusion, at the same time promising consistent reconstruction capabilities, hence the possibility to contrive an SST assisted inference engine. We are going to test it using datasets extracted from search engine trends, using a cloud of keywords related to the Bitcoin topic. This could be useful to study the evolution of the cryptocurrency both in time and geographical terms, and to estimate the future number of queries. The importance of Bitcoin queries prediction goes beyond the academic and research environments and, as such, it could lead to valuable commercial applications, such as financial recommender systems or blockchain-based transaction managers development.
Blockchain represents a technology for establishing a shared, immutable version of the truth between a network of participants that do not trust one another, and therefore has the potential to disrupt any financial or other industries that rely on third-parties to establish trust. Recent trends in computing including: prevalence of Free and Open Source Software (FOSS); easy access to High Performance Computing (HPC i.e. 'The Cloud'); and increasingly advanced analytics capabilities such as Natural Language Processing (NLP) and Machine Learning (ML) allow for rapidly prototyping applications for analysis of trends in the emergence of Blockchain technology. A scaleable proof-of-concept pipeline that lays the groundwork for analysis of multiple streams of semi-structured data posted on social media is demonstrated. Preliminary analysis and performance metrics are presented and discussed. Future work is described that will scale the system to cloud-based, real-time, analysis of multiple data streams, with Information Extraction (IE) (ex. sentiment analysis) and Machine Learning capability.
Recent years have seen the emergence of a new class of currencies, called\ncryptocurrencies. These currencies use cryptography to provide security\nand peer-to-peer networking to provide a decentralized system. Bitcoin is\nthe most popular of these currencies. It uses a two-pass\nSHA-256 hash at its core. Producing new bitcoins is done through a process\nreferred to as "mining", which involves a brute-force search for a hash with\na specific value. This process requires large amounts of computing power.\n\nCurrent-generation hardware for bitcoin mining includes highly-optimized\nASIC chips which provide huge amounts of performance. However, designers of\nsuch chips are having problems with delivering enough power and cooling\nto the chips. To alleviate this problem, this thesis looks at the possibilities\nof using heterogeneous computing to reduce power consumption and produce a more\nenergy-efficient mining solution.\n\nA SHA-256 accelerator and a DMA module is developed and integrated into a tile for\nthe Single-ISA Heterogeneous MAny-core Computer, SHMAC, and a system with\nmultiple cores is used to exploit the thread-level parallelism provided by\nthe platform. The system is tested using a benchmark to find out what performance\nand energy efficiency can be expected when using the system for bitcoin mining.\n\nThe results show a maximum performance of 175,7 kH/s when running the benchmark\napplication on 14 cores using the SHA-256 accelerator and the DMA module. The best\nenergy efficiency was obtained when running on 14 cores without the DMA enabled,\nat 163,2 kH/J. The results does not compare well to specialized FPGA-based\nbitcoin miners, but demonstrates the SHMAC platform's large degree of thread-level parallelism\nwhich can be better exploited in other applications.
Martina Matta, Maria Ilaria Lunesu, Michele Marchesi
In the last decade, Web 2.0 services such as blogs, tweets, forums, chats, email etc. have been widely used as communication media, with very good results. Sharing knowledge is an important part of learning and enhancing skills. Furthermore, emotions may affect decisionmaking and individual behavior. Bitcoin, a decentralized electronic currency system, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work, we investigated if the spread of the Bitcoin’s price is related to the volumes of tweets or Web Search media results. We compared trends of price with Google Trends data, volume of tweets and particularly with those that express a positive sentiment. We found significant cross correlation values, especially between Bitcoin price and Google Trends data, arguing our initial idea based on studies about trends in stock and goods market.
Abstract. We formalize the use of Bitcoin as a source of publicly-verifiable randomness. As a side-effect of Bitcoin’s proof-of-work-based consensus system, random values are broadcast every time new blocks are mined. We can derive strong lower bounds on the computational min-entropy in each block: currently, at least 68 bits of min-entropy are produced every 10 minutes, from which one can derive over 32 near-uniform bits using standard extractor techniques. We show that any attack on this beacon would form an attack on Bitcoin itself and hence have a monetary cost that we can bound, unlike any other construc-tion for a public randomness beacon in the literature. In our simplest construction, we show that a lottery producing a single unbiased bit is manipulation-resistant against an attacker with a stake of less than 50 bitcoins in the output, or about US$12,000 today. Finally, we propose making the beacon output available to smart contracts and demonstrate that this simple tool enables a number of interesting applications. 1
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regression for the so-called "latent source model". The Bayesian regression for "latent source model" was introduced and discussed by Chen, Nikolov and Shah (2013) and Bresler, Chen and Shah (2014) for the purpose of binary classification. They established theoretical as well as empirical efficacy of the method for the setting of binary classification. In this paper, instead we utilize it for predicting real-valued quantity, the price of Bitcoin. Based on this price prediction method, we devise a simple strategy for trading Bitcoin. The strategy is able to nearly double the investment in less than 60 day period when run against real data trace.