This study analyzes the impact of validator behavior on investor rewards in proof-of-stake (PoS) based blockchain networks and proposes a visualization system to assist investors in selecting appropriate validators. This system enables personalized evaluations through five adjustable indicators tailored to the investor's preferences. By utilizing similarity-based circular visualizations and radar charts, it facilitates the selection and comparison of validators. Additionally, it provides time-series data-based line graphs and raw data-based table views to support detailed comparative analysis among validators. The introduction of such a multifaceted evaluation methodology in staking investments is expected to contribute to the formation of user-customized portfolios and the establishment of optimized investment strategies.
Truth and Privacy-preserving service are two key issues for data collection in Mobile Crowd Sensing (MCS). However, most of the existing data collection studies use CRH to calculate the truth value, which is difficult to guarantee the data accuracy. The proposed privacy-preserving service methods either neglect the truth-value computation or still adopt the CRH method. To solve the challenge, we propose a Reputation and Privacy-Preserving Service based Truth Data Collection (RPPS-TDC) scheme to achieve the privacy-preserving accurate data collection for MCS. RPPS-TDC scheme consists of two steps: worker trust calculation and reputation-based truth value calculation. Firstly, data requester performs the initial weights calculation on the received data using DBCRH method. Secondly, reputation center uses the initial weights to update the reputation, which is based on the trustworthy worker inference. Finally, the workers are reweighted according to their reputation. We select workers based on trust_MaxHeap, thus obtaining more accurate data. At the same time, we give workers payments based on their trust weights. All the above operations are completed under data encryption, ensuring that only the data requesters have access to the workers’ submitted data. The platform consists of mining nodes, and reputation center resides on the blockchain, thus achieving distributed computing which overcomes the shortcomings of the traditional MCS system. Extensive experiments demonstrate that RPPS-TDC is effective and outperforms previous strategies in two key performance metrics: data accuracy and compensation rationality allocation.
Stefano Ferretti, Gabriele D’Angelo, Vittorio Ghini
This paper presents a study on the application of Heterogeneous Graph Neural Networks (HGNNs) for enhancing the security of complex social systems by identifying illicit and malicious behaviors. We focus on digital asset tokenization, a key component in the construction of many innovative social services, with the aim of classifying token exchanges and identifying illicit activities. Utilizing the Elliptic++ dataset, we demonstrate the efficacy of HGNNs in identifying illicit activities in token-based exchanging applications. In particular, we evaluate four different HGNN architectures, i.e. Heterogeneous GAT, Heterogeneous SAGE, HGT (Heterogeneous Graph Transformer), and HAN (Heterogeneous Attention Network). Our results underscore the importance of characterizing and describing interactions in these complex systems, both for studying the system dynamics and for activating mechanisms to cope with cybersecurity issues, like misuses and usurpation of resources in social systems.
In the Bitcoin blockchain, an entity (e.g., a gambling service) may control multiple distinct address clusters. Links (i.e., trust relationships) between these disjoint address clusters can be established when one cluster is abandoned, and a new one is formed shortly thereafter. To link the clusters across time, we have developed a deep neural network model that exploits these synchronous actions derived from unlabeled data in a self-supervised manner. This model assesses whether two clusters exhibit synchronous temporal signatures indicative of a shared entity ownership.
In this article, network analysis has been employed to study the dynamic evolution of the cryptocurrency market from 1 January 2020 to 1 January 2024. This approach facilitates an in-depth exploration of the market’s response to several major events during this period, including the coronavirus disease of 2019 (COVID-19) pandemic and the bankruptcy of FTX, one of the largest cryptocurrency exchanges. The study focuses on analysing key network characteristics of the cryptocurrency market, namely: (a) degree centrality, (b) betweenness centrality, (c) clustering coefficient and (d) average path length. Additionally, we explore the co-movements within the market, categorising cryptocurrencies into functional groups for a comparative analysis. This approach enables us to examine shifts in the cryptocurrency network topology, providing insights into how different groups of cryptocurrencies interact with and influence each other. Through this network analysis, we aim to shed light on the intricate interrelationships among cryptocurrencies. The findings of this study are intended to provide investors with valuable insights, potentially guiding the development of more informed and strategic diversification strategies in the dynamic and evolving landscape of the cryptocurrency market. JEL Codes: G11, G12, D85
The Lightning Network (LN) emerged in recent years as the most promising solution for scaling the Bitcoin network on a second layer. While the protocol specifications reached a relative maturity level, research efforts on higher-level challenges such as topology discovery, routing, and adversarial scenarios are still in their infancy. On the one hand, traditional network modelling approaches ignore the peculiarity of LN, that is, being based on a time-flow dictated by Bitcoin layer events. On the other, deploying thousands of actual nodes would require massive computational resources and, especially from an academic perspective, non-trivial interventions on complex production-stage code to test more experimental scenarios. With this work, we introduce the concept of Timechain-Ievel model, along with an open-source implementation, to abstract the complexity of the Lightning Network while still providing a vision of base layer events and protocol internals. After describing how each element of the model is mapped into the LN architectural stack, we show a case study to demonstrate its usage in investigating large-scale scenarios for research, development and educational purposes. Finally, we present a detailed comparison to properly contextualize our contribution to the current state of the art of LN modelling, highlighting the advancements introduced by a Timechain-level and future directions of research it opens.
Christian Nedu Osakwe, Oluwatobi A. Ogunmokun, Islam Elgammal, Darya Baeva · 5 authors
Abstract This article adopts the value‐attitude‐behavioural (VAB) and attitude‐behaviour‐context (ABC) theoretical lenses to develop an integrative model to examine attitudinal and behavioural responses to cryptocurrency investment. It also investigates the moderating role of generational differences (pre‐millennials vs. millennials). The study showed that perceived value is closely associated with the attitude towards cryptocurrency investment which, in turn, is strongly associated with the willingness to make and recommend cryptocurrency investments. Results further reveal that contextual factors such as convertibility and sugrophobia, which reflect the fear of being duped, strongly influence individuals' willingness to recommend cryptocurrency investments to others. Finally, results indicate that generational differences play an important moderating role.
Strategies related to the blockchain concept of Extractable Value (MEV/BEV), such as arbitrage, front-, or back-running create strong economic incentives for network nodes to reduce latency. Modified nodes, that minimize transaction validation time and neglect to filter invalid transactions in the Ethereum peer-to-peer (P2P) network, introduce a novel attack vector -- a Blockchain Amplification Attack. An attacker can exploit those modified nodes to amplify invalid transactions thousands of times, posing a security threat to the entire network. To illustrate attack feasibility and practicality in the current Ethereum network ("mainnet"), we 1) identify thousands of similar attacks in the wild, 2) mathematically model the propagation mechanism, 3) empirically measure model parameters from our monitoring nodes, and 4) compare the performance with other existing Denial-of-Service attacks through local simulation. We show that an attacker can amplify network traffic at modified nodes by a factor of 3,600, and cause economic damages of approximately 13,800 times the amount needed to carry out the attack. Despite these risks, aggressive latency reduction may still be profitable enough for various providers to justify the existence of modified nodes. To assess this trade-off, we 1) simulate the transaction validation process in a local network and 2) empirically measure the latency reduction by deploying our modified node in the Ethereum test network ("testnet"). We conclude with a cost-benefit analysis of skipping validation and provide mitigation strategies against the blockchain amplification attack.
Yan Wu, Liuyang Zhao, Jia Zhang, Leilei Shi · 6 authors
Bitcoin transaction analysis is valuable for examining Bitcoin events. However, most of the existing methods are inadequate for dealing with transactions involving multiple entities. Furthermore, existing Bitcoin transaction analysis methods neglect to evaluate the influence of different entities on a Bitcoin event. This article aims to overcome such limitations by introducing a novel method for multi-entity Bitcoin transaction analysis along with proposing a method for multi-entity influence assessment based on the Bitcoin transaction network (BTN) model. To overcome the loss of tracking information, a Bitcoin gene operation named compound dyeing is devised and incorporated into the BTN simulation. After obtaining the simulation results, a method for multi-entity transaction behavior analysis is presented to identify and visualize the interactions among entities precisely and effectively. Furthermore, four influence indices with suitable visualization methods are proposed based on the features of the BTN to measure the business and trading influences of different entities. A real-world case study, the Mt.Gox coin loss event, is analyzed to demonstrate the effectiveness and efficiency of the proposed methods.
Togzhan Barakbayeva, Zhuo Cai, Amir Kafshdar Goharshady, Karaneh Keypoor
Correlated equilibria are a standard solution concept in game theory and generalize Nash equilibria. In a 2-player non-cooperative game in which player i has action set A_i, a correlated equilibrium is a self-enforcing probability distribution σ over A_1 * A_2. Specifically, when a strategy profile (s_1, s_2) in A_1 * A_2 is sampled according to σ, each player i can observe their own component s_i, but not the other player's component. Knowing s_i and σ, player i cannot increase their expected payoff by defecting and playing a strategy s'_i different from s_i. Correlated equilibria are ubiquitous and crucial in mechanism design, including in the design of blockchain-based protocols which aim to incentivize honest behavior. A correlated equilibrium depends on a centralized and impartial oracle, often called the ''external signal'' in game theory literature, to sample a strategy profile and disclose each player's component to them, while keeping the other player's component secret. However, there is currently no trustless method to achieve this on the blockchain without centralization or relying on trusted third-parties. In this work, we address this challenge and provide two novel protocols, one based on oblivious transfer and the other based on zkSNARKs to replace the public signal with a smart contract. We prove that our approaches are secure and provide the desired privacy properties of a correlated equilibrium, while also being efficient in terms of gas usage and thus affordable in practice.
Cheick Tidiane Bâ, Richard G. Clegg, Benjamin A. Steer, Matteo Zignani
In the dynamic landscape of the Web, we are witnessing the emergence of the Web3 paradigm, which dictates that platforms should rely on blockchain technology and cryptocurrencies to sustain themselves and their profitability. Cryptocurrencies are characterised by high market volatility and susceptibility to substantial crashes, issues that require temporal analysis methodologies able to tackle the high temporal resolution, heterogeneity and scale of blockchain data. While existing research attempts to analyse crash events, fundamental questions persist regarding the optimal time scale for analysis, differentiation between long-term and short-term trends, and the identification and characterisation of shock events within these decentralised systems. This paper addresses these issues by examining cryptocurrencies traded on the Ethereum blockchain, with a spotlight on the crash of the stablecoin TerraUSD and the currency LUNA designed to stabilise it. Utilising complex network analysis and a multi-layer temporal graph allows the study of the correlations between the layers representing the currencies and system evolution across diverse time scales. The investigation sheds light on the strong interconnections among stablecoins pre-crash and the significant post-crash transformations. We identify anomalous signals before, during, and after the collapse, emphasising their impact on graph structure metrics and user movement across layers. This paper pioneers temporal, cross-chain graph analysis to explore a cryptocurrency collapse. It emphasises the importance of temporal analysis for studies on web-derived data and how graph-based analysis can enhance traditional econometric results. Overall, this research carries implications beyond its field, for example for regulatory agencies aiming to safeguard users from shocks and monitor investment risks for citizens and clients.
This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.
The rise of prosumers – individuals who both produce and consume energy – presents a significant opportunity to reshape energy markets and achieve carbon neutrality. However, current energy trading models struggle to effectively track emissions and incentivize sustainable consumption behaviors. This study introduces a novel, blockchain-based peer-to-peer (P2P) platform for trading carbon allowances, designed to empower prosumers and revolutionize energy consumption patterns. Utilizing blockchain technology, the platform enables direct, transparent, and secure transactions between prosumers, creating a decentralized market where they can set their own prices for carbon allowances. This dynamic and competitive environment empowers prosumers to take control of their energy consumption and incentivizes the adoption of sustainable practices. The platform also incorporates a decentralized reward system targeting specific consumption habits, promoting behaviors that reduce carbon emissions. Empirical evidence and theoretical justification within the study highlight the platform’s potential to transform energy consumption patterns. The transparent and verifiable nature of blockchain technology addresses the limitations of existing centralized and aggregator-based trading methods. The proposed platform provides a robust framework for tracking carbon emissions, promoting sustainable consumption, and empowering prosumers to actively participate in the energy transition. This innovative solution addresses the challenges faced by prosumers in the energy market, paving the way for a more sustainable and equitable future.
The global cryptocurrency market has witnessed substantial growth, projected to expand from $910.3 million in 2021 to $1,902.5 million by 2028, with a compound annual growth rate (CAGR) of 11.1% during the forecast period. Notably, the United States leads in revenue generation, expected to reach US$23,220.00 million in 2024. With an estimated 992.50 million users by 2028, the market's trajectory indicates increasing adoption worldwide, particularly in developing nations where digital currencies serve as emerging financial exchange mediums. The surge in popularity of digital assets, such as Bitcoin and Litecoin, alongside their integration with Blockchain technology for decentralized and efficient transactions, propels market expansion. Furthermore, Artificial Intelligence (AI) advancements have begun reshaping the cryptocurrency landscape, with AI-based platforms gaining prominence and driving innovation. The growing acceptance of cryptocurrencies as legitimate payment methods by businesses, including major corporations like Tesla Inc. and MasterCard Inc., further accelerates the market growth. This research paper explores the significance of cryptocurrencies, analyzes the fluctuations of leading cryptocurrencies, and elucidates the diverse factors influencing their value, thus contributing to a deeper understanding of this dynamic and evolving market landscape. The findings highlight the complex interplay of these factors, offering insights into the dynamics of cryptocurrency markets and guiding future investment decisions. This comprehensive analysis provides a nuanced understanding of the cryptocurrency landscape, emphasizing both opportunities and inherent risks.
Blockchain, as an emerging technology, has gained widespread research in academia and industry due to its decentralization and traceability. As an important form of blockchain, consortium chains are often applied in the Internet of Things (IoT) to ensure the authenticity and reliability of data. Within consortium chains, the practical Byzantine fault tolerance (PBFT) method is a key technology for ensuring the data consistency. It plays a central role in enhancing the system performance, security, and scalability. However, with the increase in the number of user nodes and the diversification of application scenarios, PBFT faces significant challenges in maintaining performance and security, particularly due to the increased communication overhead, longer consensus latency (CL), and risks of malicious attacks on the leader node. To overcome these challenges, this article proposes a new blockchain consensus mechanism, namely the reputation awareness randomization consensus mechanism in the blockchain systems (RARCs). This mechanism first builds an evaluation model for the nodes, dividing them into ordinary nodes and candidate nodes through the reputation assessment. Second, it constructs a consensus node selection strategy to select the high-quality consensus nodes from the candidate nodes. Finally, RARC establishes a leader node randomization selection mechanism, increasing the unpredictability of the leader node and reducing the probability of the malicious attacks. Through the theoretical analysis and simulation experiments, we demonstrate that the RARC can significantly reduce the CL, enhance the throughput, and increase the unpredictability of the leader node, thereby improving the performance and security of the blockchain systems.
A novel approach of gravity strength centrality (GSC) model is proposed to identify the influential risk spreaders in cryptocurrency networks. We also validate the performance of GSC model in terms of discrimination and accuracy using methods such as individuation rate, imprecision function, and Kendall coefficient. Our findings show that (i) the novel GSC model can serve as an excellent indicator for measuring risk spreaders in the cryptocurrency market, (ii) Ethereum has a stronger influence compared to Bitcoin,(iii) the role of cryptocurrency EOS is increased after the COVID-19 pandemic.
Blockchain technology, serving as the backbone for decentralized systems, facilitates secure and transparent transactional data storage across a distributed network of nodes. Blockchain platforms rely on distributed ledgers to enable secure peer-to-peer transactions without central oversight. As these systems grow in complexity, analyzing their topological structure and vulnerabilities requires robust mathematical frameworks. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities. Attack vectors are assessed by simulating adversarial scenarios within graph models of blockchain systems. Overall, translating blockchain ecosystems into graph representations allows comprehensive analytical insights to guide the development of efficient, resilient decentralized infrastructures.