Natkamon Tovanich, Nicolas Heulot, Jean‐Daniel Fekete, Petra Isenberg
We contribute a systematic review of online visualizations of the Bitcoin blockchain. Bitcoin is currently the most active cryptocurrency with the largest market share among other cryptocurrencies. It has attracted a large user base and more and more businesses are beginning to accept Bitcoin as payment. While there are still relatively few visualization research papers on Bitcoin, a growing number of online tools visualize data about the Bitcoin blockchain. We provide a first systematic assessment of these online tools to inform future research efforts on making the Bitcoin blockchain more accessible.
The first six months of 2018 saw cryptocurrency thefts of $761 million, and the technology is also the latest and greatest tool for money laundering. This increase in crime has caused both researchers and law enforcement to look for ways to trace criminal proceeds. Although tracing algorithms have improved recently, they still yield an enormous amount of data of which very few datapoints are relevant or interesting to investigators, let alone ordinary bitcoin owners interested in provenance. In this work we describe efforts to visualize relevant data on a blockchain. To accomplish this we come up with a graphical model to represent the stolen coins and then implement this using a variety of visualization techniques.
Learning analytics and data mining require gathering and exchanging learner data for further processing and designing of activities tailored to learner’s characteristics, context, and needs. Currently, systems that store learners’ attributes should, ideally, be operated and controlled by responsible and trustworthy authorities that guarantee the protection and sovereignty of data and use objective criteria to protect and represent all parties’ interests. This chapter introduces a peer-to-peer method for storing and exchanging learner data with minimal trust. The proposed approach, underpinned by the Experience API standard, eliminates the need of a mediator authority by using distributed ledger technology.
Aiming at the problem of illegal activities of Bitcoin, in this paper, transaction data structure of Bitcoin and data storage mechanism of Bitcoin Core (official client for Bitcoin) and are firstly analyzed, then a MapReduce-based structured method for processing transaction data from Bitcoin Core is introduced. Based on above knowledge, mechanisms of tracking and tracing Bitcoin fund flows are proposed, along with the visualization powered by D3.js. This paper provides a practical tool for researching and analyzing flows of illegal transaction funds.
The emerging prosperity of cryptocurrencies, such as Bitcoin, has come into the spotlight during the past few years. Cryptocurrency exchanges, which act as the gateway to this world, now play a dominant role in the circulation of Bitcoin. Thus, delving into the analysis of the transaction patterns of exchanges can shed light on the evolution and trends in the Bitcoin market, and participants can gain hints for identifying credible exchanges as well. Not only Bitcoin practitioners but also researchers in the financial domains are interested in the business intelligence behind the curtain. However, the task of multiple exchanges exploration and comparisons has been limited owing to the lack of efficient tools. Previous methods of visualizing Bitcoin data have mainly concentrated on tracking suspicious transaction logs, but it is cumbersome to analyze exchanges and their relationships with existing tools and methods. In this paper, we present BitExTract, an interactive visual analytics system, which, to the best of our knowledge, is the first attempt to explore the evolutionary transaction patterns of Bitcoin exchanges from two perspectives, namely, exchange versus exchange and exchange versus client. In particular, BitExTract summarizes the evolution of the Bitcoin market by observing the transactions between exchanges over time via a massive sequence view. A node-link diagram with ego-centered views depicts the trading network of exchanges and their temporal transaction distribution. Moreover, BitExTract embeds multiple parallel bars on a timeline to examine and compare the evolution patterns of transactions between different exchanges. Three case studies with novel insights demonstrate the effectiveness and usability of our system.
Petra Isenberg, Christoph Kinkeldey, Jean‐Daniel Fekete
We contribute a visual exploration system for analyzing the behavior of individual entities exchanging Bitcoins. Bitcoin is a cryptocurrency, popular for allowing pseudonymous financial transactions. The Bitcoin blockchain is the public ledger of the Bitcoin system holding data on millions of individual transactions between pseudonymous addresses. These addresses belong to individual entities such as people, services, or enterprises. Understanding how the Bitcoin system is used, however, is difficult because it is unclear which addresses belong to the same entities. Our tool addresses this
problem by clustering addresses and displaying transaction detail for individual entities
Bitcoin is a cryptocurrency and a peer-to-peer payment system, where transactions directly take place between pseudo-anonymous users, without any centralised authority. Since the block-chain (i.e., the public ledger where transactions are registered) is an example of Big Data, a straightforward visualisation is not very informative. For this reason, we employ techniques from Visual Analytics to filter out undesired information in order to obtain a tool to visually analyse the transactions and help its analysis. For instance, different views can highlight miners, or sources and leaves of bitcoin flows, together with the balance of each address and transaction. Moreover, the main view sees transactions as grouped into disconnected "islands", making it possible to focus on only one of them at once.
Advanced Steganography and Watermarking Techniques
Dan McGinn, David Birch, David Akroyd, Miguel Molina-Solana · 6 authors
This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.
Giuseppe Di Battista, Valentino Di Donato, Maurizio Patrignani, Maurizio Pizzonia · 6 authors
Bitcoin is a digital currency whose transactions are stored into a public ledger, called blockchain, that can be viewed as a directed graph with more than 70 million nodes, where each node represents a transaction and each edge represents Bitcoins flowing from one transaction to another one. We describe a system for the visual analysis of how and when a flow of Bitcoins mixes with other flows in the transaction graph. Such a system relies on high-level metaphors for the representation of the graph and the size and characteristics of transactions, allowing for high level analysis of big portions of it.
Bitcoin is an emerging crypto-currency, which is wrapped in mystery and controversy. The goal is to transform how we transfer payments. The current approach for sending money from one remote party to another is via bank deposit and transfer by check or bank transfer. PayPal and other services were developed to provide faster payments to verified individuals, but each layer in the transaction adds time, cost, and/or risk to the transaction. Users of this new digital currency proclaim the benefits of security, anonymity, and efficiency for making transactions. The functionality and structure of the Bitcoin Network is complex and often attacked for not being a suitable replacement for currency. An independent understanding can be developed of the composite Bitcoin Financial Systems of Systems architecture by considering the challenges any System of System would face. A functional analysis, employing the Systems Modeling Language (SysML), is performed on the Bitcoin System of Systems architecture to help gain an understanding of the structure and functionality, and how that relates to the key actors and use cases, for determining if the users’ expectations are aligned with the architecture.