This paper provides the first comprehensive survey of methods for inserting arbitrary data into Bitcoinâs blockchain. Historical methods of data insertion are described, along with lesser-known techniques that are optimized for efficiency. Insertion methods are compared on the basis of efficiency, cost, convenience of data reconstruction, permanence, and potentially negative impact on the Bitcoin ecosystem.
M Vaidehi, Alivia Pandit, Bhaskar Jindal, Minu Kumari · 5 authors
After the boom and bust in cryptocurrenciesâ prices in recent years, Bitcoin has been totally regarded as an investment asset. As it is highly volatile in nature, there has been a need for good predictions for carrying base investment decisions. Although current study has used machine learning for more accurate Bitcoin price prediction, some of them did focused on the feasibility of applying different modeling techniques to the samples that has different data structures and dimension features. To predict Bitcoin price on different frequencies after using machine learning techniques, firstly we have to classify the Bitcoin price with daily price and high-frequency price. Here, we attempt to predict Bitcoin price as accurately as possible by taking into consideration various protocols that affect the Bitcoin value. Using the provided data we would predict the sign of daily price change with highest possible accuracy. We have used Random Forest Classifier and compared with benchmark results as daily price prediction, we achieve a better performance, with the highest accuracies of the statistical methods and machine learning algorithms of 99%. my investigation in Bitcoin price prediction can be considered as a pilot study for the importance of the sample dimension in the machine learning techniques. Keywords Bitcoin, Crypto Currency, Machine Learning, Blockchain, Long Short Term Memory(LSTM), Recurrent Neural Network(RNN), Prediction
Giovanni Ciatto, Roberta Calegari, Stefano Mariani, Enrico Denti · 5 authors
The blockchain is a novel approach to support distributed systems enabling a common, consistent view of a shared state among distributed nodes. There, smart contracts are computer programs that allow users to deploy arbitrary computations, in charge of automatically regulate state transitions and enforce properties. In this paper we speculate on how the blockchain and smart contracts could take advantage of a logic programming approach, and, complementarily, on how logic programming can benefit from the blockchain infrastructure. Accordingly, we discuss some possible research directions and open questions for future research.
Blockchain technologies have the potential to establish novel financial service infrastructures and reshape numerous fields. A blockchain is essentially a distributed ledger maintained by a set of peers (i.e., trading nodes) that do not fully trust each other. A key challenge that blockchain faces is to precisely classify the blockchain peers into categories with respect to their behavior patterns, which will not only enable deeper insights into the blockchain network but also facilitate more effective maintenance of the various peers (in private chains). In this paper, we introduce and formulate the problem of behavior pattern classification in blockchain networks and propose a novel deep-learning-based method, termedPeerClassifier, to address the problem. To the best of our knowledge, we are the first to formally define the problem of peer behavior classification in blockchain networks. Moreover, we conduct extensive experiments to evaluate our proposed approach. Experimental results demonstrate thatPeerClassifieris significantly more effective than the existing conventional methods.
Modern IoT deployments do require considerable investments that might only be justified if the data being gathered could be monetized, which leads to the need for a digital data marketplace. In many cases, the provider of the IoT data needs to process it locally for data curation, aggregation, stream processing, etc. At the same time, the consumer could be interested in nearby data. This scenario resembles a fog computing architecture where companies require being able, keeping data under their control, to securely make it available to other companies in a peerâtoâpeer fashion, without needing a cloud intermediary (like traditional marketplaces do), thus maximizing the locality of the processing and avoiding the existence of a bottleneck when the intermediary makes the data delivery for accounting purposes. Nevertheless, this imposes a hard requirement: by not having a central marketplace, the peers (seller and customer) need to trust each other, which, in turn, requires enforcing a nonrepudiation schema. In this paper, the authors propose a distributed peerâtoâpeer architecture for such a data marketplace that takes advantage of the architectural fundamentals of fog computing, in which data processing, filtering, and stream based event generation is done in a fog node along with the data, and where relationships, both commercial agreements and data delivery, are performed directly between producers and consumers without the need of mutual trust thanks to the usage of blockchain principles (e.g., distributed ledger, consensus mechanism). The proposed architecture is validated through a case study involving a set of key issues regarding nonrepudiation commonly identified when moving from a centralized marketplace to a distributed one. Moreover, it is shown that the proposed solution does not bring in any limitation with regard to a centralized marketplace solution, in terms of pricing models (subscriptions, payâperâuse, etc.) or usage conditions (contract duration, updates rate, etc.).
CĂŒneyt GĂŒrcan Akçora, Matthew Dixon, Yulia R. Gel, Murat KantarcıoÄlu
A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses.
The concept of Bitcoin was first introduced by an unknown individual (or a group of people) named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is a peer-to-peer cryptocurrency and a decentralized worldwide payment system for digital currency where transactions take place among users without any intermediary. Bitcoin transactions are performed and verified by network nodes and then registered in a public ledger called blockchain, which is maintained by network entities running Bitcoin software. To date, this cryptocurrency is worth close to U.S. $150 billion and widely traded across the world. However, as Bitcoin's popularity grows, many security concerns are coming to the forefront. Overall, Bitcoin security inevitably depends upon the distributed protocols-based stimulant-compatible proof-of-work that is being run by network entities called miners, who are anticipated to primarily maintain the blockchain (ledger). As a result, many researchers are exploring new threats to the entire system, introducing new countermeasures, and therefore anticipating new security trends. In this survey paper, we conduct an intensive study that explores key security concerns. We first start by presenting a global overview of the Bitcoin protocol as well as its major components. Next, we detail the existing threats and weaknesses of the Bitcoin system and its main technologies including the blockchain protocol. Last, we discuss current existing security studies and solutions and summarize open research challenges and trends for future research in Bitcoin security.
Mehrdad Salimitari, Mainak Chatterjee, Murat YĂŒksel, Eduardo L. Pasiliao
It is predicted that cryptocurrencies will play an important role in the global economy. Therefore, it is prudent for us to understand the importance and monetary value of such cryptocurrencies, and strategize our investments accordingly. One of the ways to obtain cryptocurrency is via mining. As solo mining is not possible because of the computational requirements, pool mining has gained popularity. In this paper, we focus on Bitcoin and its pools. With more than 20 pools in the network of Bitcoin and other cryptocurrencies, it becomes challenging for a new miner to decide the pool he must join such that the profit is maximized. We use prospect theory to predict the profit that a specific miner, given his hash rate power and electricity costs, is expected to make from each pool. A utility value is calculated for each pool based on its recent performance, hash rate power, total number of the pool members, reward distribution policy of the pool, electricity fee in the new miner's region, pool fee, and the current Bitcoin value. Then, based on these parameters during a certain time duration, the most profitable pool is found for that miner. We show how the utility values from a pool varies with electricity fee and dollar equivalent of a Bitcoin. To find the accuracy of our predictions, we mine Bitcoin by joining 5 different pools- AntPool, F2Pool, BTC.com, Slushl'ool, and BatPool. Using an Antminer 55 for each pool, we mine Bitcoin for 40 consecutive days. Results reveal that our prospect theoretic predictions are consistent with what we actually mine; however predictions using expected utility theory are not as close.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
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 âforgottenâ 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
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.
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 Bitcoinâs 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.
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.