Naman Goel, Cyril van Schreven, Aris Filos-Ratsikas, Boi Faltings
Blockchain based systems allow various kinds of financial transactions to be executed in a decentralized manner. However, these systems often rely on a trusted third party (oracle) to get correct information about the real-world events, which trigger the financial transactions. In this paper, we identify two biggest challenges in building decentralized, trustless and transparent oracles. The first challenge is acquiring correct information about the real-world events without relying on a trusted information provider. We show how a peer-consistency incentive mechanism can be used to acquire truthful information from an untrusted and self-interested crowd, even when the crowd has outside incentives to provide wrong informations. The second is a system design and implementation challenge. For the first time, we show how to implement a trustless and transparent oracle in Ethereum. We discuss various non-trivial issues that arise in implementing peer-consistency mechanisms in Ethereum, suggest several optimizations to reduce gas cost and provide empirical analysis.
Industries, household communities consume a lot of water on regular basis, thereby increasing water crisis. There is continuous increase in water consumption by industries. Reusing wastewater can reduce water withdrawals from local water sources thus increasing water availability, lowering wastewater discharges and their pollutant load, reducing thermal energy consumption and, potentially, processing cost. Various ways have been implemented for recycling the generated wastewater. Wastewater must be reused for the benefit of mankind. In this paper we propose a wastewater recycle control system to efficiently manage the wastewater and coordinate it among the industries and the government. Blockchain technology has been deployed for storing data and developing an incentive model to encourage wastewater reuse. Tokens are provided to industries in proportion to quantity and quality of reused wastewater. Rules for issue and trade of these tokens are written as a smart contract. Unfortunately, providing such incentives also provides a motive for tampering the data on which these tokens are awarded. Anomaly detection algorithms are used to detect the potential frauds which take place in the system upon IoT meter data tampering. The system uses IoT meters that measure volume of wastewater generated and reused, along with quality metrics such as pH, hardness and oil content. Multiple machine learning algorithms are used to detect tampering- polynomial regression, DBSCAN, autoencoders and LSTM networks. Their performance has then been compared. A first implementation of this system and an evaluation of the system's performance are also presented.
Blockchain is a disruptive technology that enables disparate users to share their information in blocks trustworthily without a centralized entity. One fundamental problem is how to stable the block interval. To address this problem, our method is: 1. predict the computing power (i.e., hashrate) of a blockchain system by the cryptocurrency price; 2. stable the interval according to the predicted power. This paper focuses on the prediction of the global computing power. In our prediction, we adopt a LSTM-based regression algorithm to handle the hysteresis of computing power changes in response to the price changes. Taking the Bitcoin system as an example, we run extensive experiments that verify that our prediction algorithm is very accurate.
Lun Li, Arab Ali, Jiqiang Liu, Jingxian Liu · 5 authors
Although Bitcoin and other cryptocurrencies continue to attract attention, the inaccurate pricing of Bitcoin options has resulted in an inefficient crypto derivative market. In this paper, we adopt a multiple input LSTM-based prediction model in conjunction with the Black-Scholes (BS) model to address this challenge in Bitcoin option pricing. We discuss the relationship between on/off-chain transactions and predict the implied price volatility of the next 30 days, which is the main factor in the BS model. First, we analyze the Blockchain statistics and social network trends of Bitcoin as inputs to our model, including the liveness of Blockchain wallet, the scale of active Blockchain nodes and the impact factor of Google, Reddit, Twitter to name a few. Next, we implement the LSTM-based prediction model and evaluate it in various historical window sizes and network parameters. Finally, we compare the performance with the baseline model without Blockchain statistics inputs. The experimental results show that the proposed multi-input LSTM-based prediction model provides the risk-informed pricing of the Bitcoin call options and our Blockchain statistics reduce the root-mean-square error (RMSE) by up to 46.2%.
Ethereum is the largest blockchain platform supporting smart contracts with the second biggest market capitalization. Ethereum data can yield many useful insights because of the large volume of transactions, accounts and blocks as well as the popular applications developed as smart contracts. Studying Ethereum data can also reveal many new attacks to the platform and its smart contracts. Unfortunately, it is non-trivial to systematically explore Ethereum because it involves massive heterogeneous data, which are produced and stored in different ways. Although a few recent studies report some interesting observations about Ethereum, they are limited by their data acquisition methods which cannot provide comprehensive and precise data. In this paper, to fill the gap, we propose DataEther, a systematic and high-fidelity data exploration framework for Ethereum by exploiting its internal mechanisms. Besides supporting the analyses in existing studies, DataEther further empowers users to explore unknown phenomena and obtain in-depth understandings. We first describe how we tackle the challenging issues in developing DataEther, and then use four data-centric applications to demonstrate its usage and report many new observations.
Predicting the trends in Bitcoin market prices is a very challenging task due to the many uncertainties and variables influencing the market value. The market is susceptible to quick changes, causing seemingly random fluctuations in the Bitcoin price. Due to the chaotic and highly volatile nature of Bitcoin behavior, investments come with high risk. To minimize the risk involved, knowledge of the Bitcoin price movement in the future is desirable. Different studies have shown that Machine Learning algorithms can predict, to varying degrees, the price fluctuations of Bitcoin. However, most researches do not explore the relationship between the price and other features outside the transaction network, such as market capitalization, Bitcoin mining speed, or entity behavior. Also, most of the features are extracted from the network level, which means obtaining the number of transactions, users, Bitcoins mined, etc. In this research, we focus on additional features, such as features outside the transaction network and node-based features inside the transaction network, which could improve the price prediction of Bitcoin. The investigated features are the “fairness and goodness” measure and the “1-ARW-betweenness cen- trality” measure. Fairness and goodness are entity behavior measures. The goodness of a Bitcoin address captures how much this address is liked/trusted by other addresses, while the fairness of a Bitcoin address captures how fair the address is in rating other addresses’ likeability or trust level. The 1-ARW-betweenness centrality is a feature based on absorbing random walks. The feature captures the extent to which a Bitcoin address has control over the money flow between different addresses. A benchmark, based on the machine learning algorithm Random Forest with commonly used features, is used to test the impact of the additional features. The Random Forest tries to predict the sign (up-down movement) of the price per day, using data from the two previous days. Comparing this benchmark with a similar model, but then including the additional features, will gain more information about how these additional features influence the Bitcoin price.
Η ιδέα όπου υλοποιείται σε αυτή την εργασία, είναι η δημιουργία ενός μοντέλου Μηχανικής Μάθησης, το οποίο αξιοποιεί παρελθοντικές τιμές του Bitcoin, δεδομένα τάσεων της Google και χαρακτηριστικά, τα οποία δημιουργήθηκαν με εξόρυξη γνώσης απο tweets σχετικά με το Bitcoin. Σκοπός αυτής της μελέτης είναι η πρόβλεψη των μελλοντικών τιμών του Bitcoin. Για το σκοπό αυτό, συγκρίνεται ένα Βαθύ Νευρωνικό Δίκτυο και συγκεκριμένα ένα δίκτυο με στρώματα LSTM, ένα μοντέλο Παλινδρόμησης Ενίσχυσης Κλίσης και μοντέλο XGBoost. Σύμφωνα με τα αποτελέσματα, το Βαθύ Νευρωνικό Δίκτυο είχε καλύτερη απόδοση, με ρίζα μέσου τετραγωνικού σφάλματος 0.999 % στα δεδομένα ελέγχου.
Wattana Viriyasitavat, Li Da Xu, Zhuming Bi, Danupol Hoonsopon · 5 authors
Owing to the exponential growth of Internet of Things (IoTs), ensure that the Quality of Service (QoS) over IoT becomes challenges at the network edge or on cloud. The traditional mechanisms for QoS measurements rely on the centralized trusted third parties who use specialized agents to collect data and measure the performances of services. However, these mechanisms are ineffective to deal with highly dynamic and distributed nature of IoT-based services. Moreover, the dynamism of QoS needs to collect, update, and access reliable quality relevant data frequently, while lacking trust becomes a major hurdle for data utilization. It is our argument that the QoS measurement of IoT-based services would be decentralized and the trusts be built from collectively trusted subnetworks. In this paper, we propose to integrate the blockchain technologies (BCT) with a multi-agent approach to warrantee the trustiness of real-time data for the measurement of QoS in the IoT environment. The proposed approach is verified by some demonstrative examples in addressing QoS specification patterns commonly found in service-based applications (SBAs), where qualitative analyses are conducted for the evaluation of the patterns.
Mohamad Arafeh, May El Barachi, Azzam Mourad, Fatna Belqasmi
With the emergence of mobile crowdsensing (MCS), we now have the possibility of leveraging the sensing capabilities of mobile devices to collect information and intelligence about cities and events. Despite the promise that MCS brings, this new concept opens the door to a multitude of security and privacy threats and attacks. Indeed, the human involvement in the crowdsensing process and the openness of this process to any participant, render the task of securing MCS environments very challenging. In this work, we propose a Blockchain-based hybrid architecture for the detection and prevention of fake sensing activities in MCS. Our architecture leverages the capabilities of the Blockchain network and introduces a new role to the MCS architecture to ensure the validation of the collected information. Combining both data quality metrics along with behavioral analysis based participants' reliability scoring, our solution is able to detect variations in behavior and quality of contributions. The proposed solution was tested with real life data collected from 200 mobile users, over the span of 2 years, and the results obtained are very promising.
Francesco Zola, Cristina Pérez‐Solà, Jon Egana, Maria Eguimendia · 5 authors
Bitcoin is a peer-to-peer distributed cryptocurrency system, that keeps all transaction history in a public ledger known as blockchain. The Bitcoin network is implicitly pseudoanonymous and its nodes are controlled by independent entities making network analysis difficult. This calls for the development of a fully controlled testing environment. This paper presents Kriptosare.gen, a dockerized automatized Bitcoin testbed, for deploying full-scale custom Bitcoin networks. The testbed is deployed in a single machine executing four different experiments, each one with different network configuration. We perform a cost analysis to investigate how the resources are related with network parameters and provide experimental data quantifying the amount of computational resources needed to run the different types of simulations. Obtained results demonstrate that it is possible to run the testbed with a configuration similar to a real Bitcoin system.
Blockchain technology, and more specifically Bitcoin (one of its foremost applications), have been receiving increasing attention in the scientific community. The first publications with Bitcoin as a topic, can be traced back to 2012. In spite of this short time span, the production magnitude (1162 papers) makes it necessary to make a bibliometric study in order to observe research clusters, emerging topics, and leading scholars. Our paper is aimed at studying the scientific production only around bitcoin, excluding other blockchain applications. Thus, we restricted our search to papers indexed in the Web of Science Core Collection, whose topic is "bitcoin". This database is suitable for such diverse disciplines such as economics, engineering, mathematics, and computer science. This bibliometric study draws the landscape of the current state and trends of Bitcoin-related research in different scientific disciplines.
The Internet of Things (IoT) is a remarkable data producer and these data may be used to prevent or detect security vulnerabilities and increase productivity by the adoption of statistical and Artificial Intelligence (AI) techniques. However, these desirable benefits are gained if data from IoT networks are dependable—this is where blockchain comes into play. In fact, through blockchain, critical IoT data may be trusted, i.e., considered valid for any subsequent processing. A simple formal model named “the Mirror Model” is proposed to connect IoT data organized in traditional models to assets of trust in a blockchain. The Mirror Model sets some formal conditions to produce trusted data that remain trusted over time. A possible practical implementation of an application programming interface (API) is proposed, which keeps the data and the trust model in synch. Finally, it is noted that the Mirror Model enforces a top-down approach from reality to implementation instead of going the opposite way as it is now the practice when referring to blockchain and the IoT.
Jelena Mišić, Vojislav B. Mišić, Xiaolin Chang, Saeideh G. Motlagh · 5 authors
In this work we provide comprehensive analytical model for Bitcoin distribution network. We apply Jackson network model on the whole Bitcoin network where individual nodes operate as priority M/G/1 queuing systems. Data arrival process to the nodes is modeled as a non-homogeneous Poisson process in which the data arrival rates to the nodes are derived from the analytical model of gossip data delivery protocol. This model considers random probability distribution of node connectivity. Performance results include network distribution time for blocks, node response time for blocks, and populations of data distribution algorithms as functions on network size. Usefulness of this model is demonstrated by efficiently computing the forking probability for the Bitcoin blockchain.
Blockchain has attracted wide attention. A smart contract is a program that runs on the blockchain, and there is evidence that most of the smart contracts on the Ethereum are highly similar, as they share lots of repetitive code. In this study, we empirically study the repetitiveness of the smart contracts via cluster analysis and try to extract the differentiated code from the similar contracts. Differentiated code is defined as the source code except the repeated ones in two similar smart contracts, which usually illustrates how a software feature is implemented or a programming issue is solved. Then, differentiated code might be used to guide the update of a smart contract in its next version. In this paper, to support the update of a target smart contract, we apply syntax and semantic similarities to discover its similar smart contracts from more than 120,000 smart contracts, and recommend the differentiated code to the target smart contract. The promising experimental results demonstrated the differentiated code can effectively support smart contract update.
We study the applicability of blockchain technology for distributed event detection under resource constraints. Therefore we provide a test-suite with several promising consensus methods (Proof-of-Work, Proof-of-Stake, Distributed Proof-of-Work, and Practical Proof-of-Kernel-Work). This is the first work analyzing the communication costs of blockchain consensus methods for knowledge discovery tasks in resource constraint devices. The experiments reveal that our proposed implementations of Distributed Proof-of-Work and Practical Proof-of-Kernel-Work provide a benefit over Proof-of-Work in CPU usage and communication costs. The tests show further that in cases of low data rates, where latencies by mining do not cause harm proposed blockchain implementations could be integrated. However, usage of blockchain requires data broadcasts, which leads to communication overhead as well as memory requirements based on the address list.
Wilson S. Melo, Alysson Bessani, Nuno Neves, Altair O. Santin · 5 authors
In recent years, measuring instruments have become quite complex due to the integration of embedded systems and software components and the increasing aggregation of new features. Consequently, metrological regulation and control require more efforts from notified bodies, becoming slower and more expensive. In this paper, we evaluate the use of blockchains as a resource to overcome such challenges. We start with a conceptual model for implementing measuring instruments in a distributed blockchain-based architecture and compare it with traditional measuring instruments and distributed measuring models discussed in previous works. We also made a security analysis, demonstrating that blockchain-based measuring systems can impact the way measuring instruments are used in consumer relations while improving security and simplifying metrological regulation and control. We implement a vehicle speed measuring system using the Hyperledger Fabric blockchain platform. We evaluate the security and performance of our blockchain-based measuring system by executing tests with data from real speed meter sensors. The results are promising and validate the feasibility of our idea. Finally, we point out the main challenges related to our approach, suggesting alternatives and potential issues to be addressed by future works.
Patrick Ocheja, Brendan Flanagan, Hiroshi Ueda, Hiroaki Ogata
It is a common practice to issue a summary of a learner’s learning achievements in form of a transcript or certificate. However, detailed information on the depth of learning and how learning or teachings were conducted is not present in the transcript of scores. This work presents the first practical implementation of a new platform for keeping track of learning achievements beyond transcripts and certificates. This is achieved by maintaining digital hashes of learning activities and managing access rights through the use of smart contracts on the blockchain. The blockchain of learning logs (BOLL) is a platform that enable learners to move their learning records from one institution to another in a secure and verifiable format. This primarily solves the cold-start problem faced by learning data analytic platforms when trying to offer personalized experience to new learners. BOLL enables existing learning data analytic platforms to access the learning logs from other institutions with the permission of the learners and/or institution who originally have ownership of the logs. The main contribution of this paper is to investigate how learning records could be connected across institutions using BOLL. We present an overview of how the implementation has been carried out, discuss resource requirements, and compare the advantages BOLL has over other similar tools.
Customer service is transforming from traditional manual service toward automated service, which utilizes different computational informatics to achieve a higher efficient and quality services. Automated customer service requires big data and expertise in data analysis as prerequisites. However, many companies, especially small and medium enterprises, do not have sufficient data and experience due to their limited scale and resources. They need to rely on third parties, and this reliance results in the lack of development of core customer service competency. In order to overcome these challenges, an open and automated customer service platform based on Internet of things (IoT), blockchain, and automated machine learning (AutoML) is proposed. The data are gathered with the use of IoT devices during the customer service. An open but secured environment to achieve data trading is ensured by using blockchain. AutoML is adopted to automate the data analysis processes for reducing the reliance of costly experts. The proposed platform is analyzed through use case evaluation. A prototype system has also been developed and evaluated. The simulation results show that our platform is scalable and efficient.
Mining pools, the main components of the Bitcoin network, dominate the computing resources and play essential roles in network security and performance aspects. Although many existing measurements of the Bitcoin network are available, little is known about the details of mining pool behaviors (e.g., empty blocks, mining revenue and transaction collection strategies) and their effects on the Bitcoin end users (e.g., transaction fees, transaction delay and transaction acceptance rate). This paper aims to fill this gap with a systematic study of mining pools. We traced over 1.56 hundred thousand blocks (including about 257 million historical transactions) from February 2016 to January 2019 and collected over 120.25 million unconfirmed transactions from March 2018 to January 2019. Then we conducted a board range of measurements on the pool evolutions, labeled transactions (blocks) as well as real-time network traffics, and discovered new interesting observations and features. Specifically, our measurements show the following. 1) A few mining pools entities continuously control most of the computing resources of the Bitcoin network. 2) Mining pools are caught in a prisoner's dilemma where mining pools compete to increase their computing resources even though the unit profit of the computing resource decreases. 3) Mining pools are stuck in a Malthusian trap where there is a stage at which the Bitcoin incentives are inadequate for feeding the exponential growth of the computing resources. 4) The market price and transaction fees are not sensitive to the event of halving block rewards. 5) The block interval of empty blocks is significantly lower than the block interval of non-empty blocks. 6) Feerate plays a dominating role in transaction collection strategy for the top mining pools. Our measurements and analysis help to understand and improve the Bitcoin network.
Open access
3 source records
Blockchain Technology Applications and Security
Data Stream Mining Techniques
Advanced Steganography and Watermarking Techniques
Evangelos Pournaras, Srivatsan Yadhunathan, Ada Diaconescu
Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.
Blockchain miners processing the transactions and generating blocks are rational and incentivized by financial rewards. Prior research in blockchain security studied such financial incentives for the miners and identified attacks based on withholding and delaying block submissions. Among such attacks, the fork-after-withholding (FAW) attack is the state of the art and provides practical incentives to the rational attacker by increasing its reward at the expense of the rest of the miners in the compromised pool. To defend against FAW attack, we propose Silent Timestamping, which has the mining pool manager enforce the ordering of the shares. Implemented at the mining pool manager, Silent Timestamping does not require networking nor any behavior-or implementation-changes in the miners; the lack of the implementation overheads distinguish Silent Timestamping from other schemes which have been proposed but had limited success in practice. We analyze the optimal attacker behavior against Silent Timestamping and the effectiveness of Silent Timestamping against FAW attack. Silent Timestamping effectively nullifies the reward gain of the FAW attack and reduces the optimal FAW attacker strategy to be either the suboptimal block-withholding attack (which never submits the withheld block in the compromised pool and provides limited incentives for a rational attacker) or honest mining.
Blockchain Technology Applications and Security
Data Stream Mining Techniques
Advanced Steganography and Watermarking Techniques
The power of machine learning in understanding the patterns in data, analyzing and making decisions, has shown its importance in various sectors. Machine Learning requires reasonable amount of data to make accurate decisions. Data sharing and reliability of data is very crucial in machine learning in order to improve its accuracy. The decentralized database in Blockchain Technology emphasizes on data sharing. The consensus in Blockchain technology makes sure that data is legitimate and secured. The convergence of these two technologies can give highly accurate results in terms of machine learning with the security and reliability of Blockchain Technology. This paper gives an overview of how combining these two technologies can help in healthcare sectors.
Saulo Ricci, Eduardo de Paulo Ferreira, Daniel Sadoc Menasché, Artur Ziviani · 6 authors
Despite the growing interest in cryptocurrencies, the delays incurred to confirm transactions are one of the factors that hamper the wide adoption of systems such as Bitcoin. Bitcoin transactions usually are confirmed in short periods (minutes), but still much larger than conventional credit card systems (seconds). In this work, we propose a framework encompassing machine learning and a queueing theory model to (i) identify which transactions will be confirmed; and (ii) characterize the confirmation time of confirmed transactions. The proposed queueing theory model accounts for factors such as the activity time of blocks and the mean time between transactions. We parameterize the model for transactions that are confirmed within minutes, suggesting that its integration into a more general framework is a step towards building scalability to Bitcoin.