Francesco Zola, Jon Ander Medina, A. Venturi, Raúl Orduna-Urrutia
Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixers remain one of the most challenging entities to tackle. This is because their owners are often unwilling to cooperate with Law Enforcement Agencies, and technically, they operate as 'black boxes'. To better understand their functionalities, this paper proposes an approach to analyse the operations of mixers by examining their address-transaction graphs and identifying topological similarities to uncover common patterns that can define the mixer's modus operandi. The approach utilises community detection algorithms to extract dense topological structures and clustering algorithms to group similar communities. The analysis is further enriched by incorporating data from external sources related to known Exchanges, in order to understand their role in mixer operations. The approach is applied to dissect the Blender.io mixer activities within the Bitcoin blockchain, revealing: i) consistent structural patterns across address-transaction graphs; ii) that Exchanges play a key role, following a well-established pattern, which raises several concerns about their AML/KYC policies. This paper represents an initial step toward dissecting and understanding the complex nature of mixer operations in cryptocurrency networks and extracting their modus operandi.
Research on Bitcoin (BTC) transactions is a matter of interest for both economic and network science fields. Although this cryptocurrency is based on a decentralized system, making transaction details freely accessible, making raw blockchain data analyzable is not straightforward due to the Bitcoin protocol specificity and data richness. To address the need for an accessible dataset, we present ORBITAAL, the first comprehensive dataset based on temporal graph formalism. The dataset covers all Bitcoin transactions from January 2009 to January 2021. ORBITAAL provides temporal graph representations of entity-entity transaction networks, snapshots, and stream graph. Each transaction value is given in Bitcoin and US dollar regarding daily-based conversion rate. This dataset also provides details on entities such as their global BTC balance and associated public addresses.
Mohsen Alambardar Meybodi, Amir Kafshdar Goharshady, M. R. Hooshmandasl, Ali Shakiba
Abstract In this work, we consider a combinatorial optimization problem with direct applications in blockchain mining, namely finding the most lucrative blocks for Bitcoin miners, and propose optimal algorithmic solutions. Our experiments show that our algorithms increase the miners’ revenues by more than a million dollars per month. Modern blockchains reward their miners in two ways: (i) a base reward for each block that is mined, and (ii) the transaction fees of those transactions that are included in the mined block. The base reward is fixed by the respective blockchain’s protocol and is not under the miner’s control. Hence, for a miner who wishes to maximize earnings, the fundamental problem is to form a valid block with maximal total transaction fees and then try to mine it. Moreover, in many protocols, including Bitcoin itself, the base reward halves at predetermined intervals, hence increasing the importance of maximizing transaction fees and mining an optimal block. This problem is further complicated by the fact that transactions can be prerequisites of each other or have conflicts (in case of double-spending). In this work, we consider the problem of forming an optimal block, i.e. a valid block with maximal total transaction fees, given a set of unmined transactions. On the theoretical side, we first formally model our problem as an extension of Knapsack and then show that, unlike classical Knapsack , our problem is strongly NP-hard. We also show a hardness-of-approximation result. As such, there is no hope in solving it efficiently for general instances. However, we observe that its real-world instances are quite sparse, i.e. the transactions have very few dependencies and conflicts. Using this fact, and exploiting three well-known graph sparsity parameters, namely treedepth, treewidth and pathwidth, we present exact linear-time parameterized algorithms that are applicable to the real-world instances and obtain optimal results. On the practical side, we provide an extensive experimental evaluation demonstrating that our approach vastly outperforms the current Bitcoin miners in practice, obtaining a significant per-block average increase of 11.34 percent in transaction fee revenues which amounts to almost one million dollars per month.
Matteo Loporchio, Damiano Di Francesco Maesa, Anna Bernasconi, Laura Ricci
Abstract The increasing adoption of tokens on the Ethereum blockchain has given rise to many distinct economic communities whose activity history is publicly accessible. In this paper we study the communities of Ethereum fungible and non-fungible tokens, regulated, respectively, by the ERC-20 and ERC-721 standards. In particular, we focus on token transfers and consider the top 100 largest ERC-20 and ERC-721 ecosystems by number of transfers, modeling them as networks where nodes correspond to participants and edges represent token transfers. We analyze their main topological properties and conduct a clustering-based study to identify groups of graphs with similar topologies. Subsequently, we classify the networks based on the application domain of their corresponding token and investigate whether graphs with similar topologies correspond to tokens within the same domain. We also conduct a temporal analysis of token popularity based on the historical transfer activity. Our findings highlight the existence of common topological properties (e.g., absence of small world effect) across both types of tokens. In contrast, the clustering analysis indicates no evident connection between the token application domain and the structure of the induced transfer networks, with the exception of non-fungible tokens associated with spamming activities.
Bitcoin, launched in 2008 by Satoshi Nakamoto, established a new digital economy where value can be stored and transferred in a fully decentralized manner - alleviating the need for a central authority. This paper introduces a large scale dataset in the form of a transactions graph representing transactions between Bitcoin users along with a set of tasks and baselines. The graph includes 252 million nodes and 785 million edges, covering a time span of nearly 13 years of and 670 million transactions. Each node and edge is timestamped. As for supervised tasks we provide two labeled sets i. a 33,000 nodes based on entity type and ii. nearly 100,000 Bitcoin addresses labeled with an entity name and an entity type. This is the largest publicly available data set of bitcoin transactions designed to facilitate advanced research and exploration in this domain, overcoming the limitations of existing datasets. Various graph neural network models are trained to predict node labels, establishing a baseline for future research. In addition, several use cases are presented to demonstrate the dataset's applicability beyond Bitcoin analysis. Finally, all data and source code is made publicly available to enable reproducibility of the results.
Sharding is a promising technique for increasing a blockchain system’s throughput by enabling parallel transaction processing. The main challenge of state sharding lies in ensuring the atomicity verification of cross-sharding transactions, which results in double communication overhead and increases the transaction’s confirmation time. Previous research has primarily focused on developing cross-shard protocols for the fast and reliable validation of transactions involving multiple shards. These studies typically generate a large number of cross-shard transactions because they primarily use simple address mapping for state sharding, that is, the prefix/suffix of the account address. In this article, we propose a state sharding scheme via density-based partitioning of the account-transaction graph. In order to reduce cross-shard transactions, the scheme groups correlated accounts into the same shard by generating the densest subgraphs, as the graph density describes the correlation among accounts, i.e., how often transactions have occurred among accounts. We formulate the graph density-based state sharding problem, with the goal of maximizing the average density across all shards under the workload constraint. We prove the NP-completeness of the problem. To reduce the complexity of finding the densest subgraph, we propose the pruning-based algorithm that reduces the search space by pre-pruning some invalid edges based on the concept of core number. We also extend the linear deterministic greedy algorithm and PageRank algorithm to handle new transactions in the dynamic scenario. We conduct extensive experiments using real transaction data from Ethereum. The experimental results demonstrate a strong correlation between the shard density and the number of cross-shard transactions, and the pruning-based algorithm can reduce the running time by an order of magnitude.
With the prosperity of the blockchain and the DeFi ecosystem, money flow activities in the blockchains are becoming increasingly frequent, complex, and diverse. The Money Flow Graph (MFG) serves as the foundation for various behavioral analysis, malicious activity detection, and money flow tracing tasks. However, traditional graph databases face the issue of storage requirement and performance when analyzing large-scale MFGs. In this work, we presentMFGScope, a lightweight domain-specific framework designed for graph-based analysis on EVM-compatible blockchains, with extensive optimizations for storage efficiency and query performance. The prototype ofMFGScopefor the Ethereum network achieves the storage of over 3 billion transfers and 1.7 billion relevant transactions in a single instance with less than 450 GB of disk usage. The evaluation shows that for common tasks,MFGScopeis more than 30 times faster and requires 78% less storage space than the commonly used graph database Neo4j. For the applications ofMFGScope, we present several use cases based on the MFG which cannot be performed efficiently using traditional graph databases and report interesting findings. To engage the community, the prototype ofMFGScopefor the Ethereum blockchain with the complete dataset will be open source.
The scalability limitations of public blockchains have hindered their widespread adoption in real-world applications. While the Ethereum community is pushing forward in zk-rollup (zero-knowledge rollup) solutions, such as introducing the ``blob transaction'' in EIP-4844, Layer 2 networks encounter a data availability problem: storing transactions completely off-chain poses a risk of data loss, particularly when Layer 2 nodes are untrusted. Additionally, building Layer 2 blocks requires significant computational power, compromising the decentralization aspect of Layer 2 networks. This paper introduces new techniques to address the data availability and decentralization challenges in Layer 2 networks. To ensure data availability, we introduce the concept of ``proof of download'', which ensures that Layer 2 nodes cannot aggregate transactions without downloading historical data. Additionally, we design a ``proof of storage'' scheme that punishes nodes who maliciously delete historical data. For decentralization, we introduce a new role separation for Layer 2, allowing nodes with limited hardware to participate. To further avoid collusion among Layer 2 nodes, we design a ``proof of luck'' scheme, which also provides robust protection against maximal extractable value (MEV) attacks. Experimental results show our techniques not only ensure data availability but also improve overall network efficiency, which implies the practicality and potential of our techniques for real-world implementation.
Cryptocurrencies created a trendsetter after the emergence of Bitcoin in the last decade. Blockchain technology is the backbone behind cryptocurrencies and gave rise to its usage in multidisciplinary fields. The future of payment with cryptocurrencies might lead to the end of manual cash. Blockchain features such as encryption, peer to peer (P2P), transparency, and timestamp make an immutable ledger to overcome the problem of a centralized system with a decentralized and trustworthy approach. Consensus algorithms are an essential component responsible for the secure flow of the network on the distributed ledger. The security and poor performance of consensus algorithms can cause hindrance for a wide range of adoption of blockchain. Drawbacks of blockchain can be addressed with a better understanding of how each consensus algorithm functions. There are various consensus algorithms according to different types of blockchains. Selection of the most appropriate consensus algorithm will improve the network performance and provide better outcomes. The analysis of varying consensus algorithms about specific metrics has been discussed. In this chapter, we will go through an 310 introduction to blockchain, the emergence of cryptocurrencies, related work done in this field, the role of consensus algorithms, comparative analysis of existing consensus algorithms, limitations of each consensus algorithm, conclusion, and future scope.
In recent years, consortium blockchain has played a significant role in fields such as logistics traceability and data transactions. However, due to the large differences in the underlying implementations of consortium blockchains, it is difficult to establish effective data sharing between different chains. Cross-chain technology is an important means to break the data isolation status of consortium blockchains, but the existing cross-chain technologies cannot meet the requirements of cross-chain supervision of consortium blockchain. There is no clear authority to divide cross-chain requests and inter-chain interoperability, and the contract calls between chains may lead to malicious tampering with ledger data during the cross-chain process, making them unsuitable for cross-chain supervision scenarios. In view of the above problems, this paper propose a cross-chain supervision mechanism of distributed notaries for consortium blockchain. The mechanism selects high-credit nodes from each consortium blockchain to serve as notaries and forms a notary chain as a medium for cross-chain sharing. By grading the identities of access chains, the security of cross-chain operations is ensured, the scalability of cross-chain is realized by modular design, and the communication message protocol designed for cross-chain supervision ensures the security of the supervision process. Finally, we compare and analyze our proposed solution with existing mechanisms and verify the practicability of the proposed scheme through simulation experiments.
The mainstream adoption of blockchains led to the preparation of many decentralized applications and web platforms, including Web 3.0, a peer-to-peer internet with no single authority. The data stored in blockchain can be considered as big data -- massive-volume, dynamic, and heterogeneous. Due to highly connected structure, graph-based modeling is an optimal tool to analyze the data stored in blockchains. Recently, several research works performed graph analysis on the publicly available blockchain data to reveal insights into its business transactions and for critical downstream tasks, e.g., cryptocurrency price prediction, phishing scams and counterfeit token detection. In this tutorial, we discuss relevant literature on blockchain data structures, storage, categories, data extraction and graphs construction, graph mining, topological data analysis, and machine learning methods used, target applications, and the new insights revealed by them, aiming towards providing a clear view of unified graph-data models for UTXO and account-based blockchains. We also emphasize future research directions.
We propose a technique to construct physical Zero-Knowledge Proof (ZKP) protocols for puzzles that require a single loop draw feature. Our approach is based on the observation that a loop has only one hole and this property remains stable by some simple transformations. Using this trick, we can transform a simple big loop, which is visible to anyone, into the solution loop by using transformations that do not disclose any information about the solution. We illustrate our technique by applying it to construct physical ZKP protocols for two Nikoli puzzles: Slitherlink and Masyu.
Since 2009, with the invention of Bitcoin, the usage of blockchain technology is constantly increasing. From its initial financial use case, blockchain as a decentralized data storage system has grown to an entirely new information ecosystem and has been successfully applied in a wide range of applications in other industrial sectors, outside of finance. Given that the data is decentralized, the computer nodes participating in the network are in charge of adding new data to the blockchain, the authenticity of which is determined by consensus algorithms as a mechanism for maintaining data integrity. Bearing that in mind, consensus algorithms and their application are crucial for the reliability and data security in a blockchain. The aim of this paper is to perform an analysis of currently most used consensus algorithms, as well as their impact on key blockchain attributes.