Blockchain Papers

Botao Zhang, Xingzhou Li, Hui Ren, Jinguang Gu

No abstract is available for this record.

Yoshino Hiromi, Nakanishi Toru

No abstract is available for this record.

Mathieu Galtier, Camille Marini

Machine learning is promising, but it often needs to process vast amounts of\nsensitive data which raises concerns about privacy. In this white-paper, we\nintroduce Substra, a distributed framework for privacy-preserving, traceable\nand collaborative Machine Learning. Substra gathers data providers and\nalgorithm designers into a network of nodes that can train models on demand but\nunder advanced permission regimes. To guarantee data privacy, Substra\nimplements distributed learning: the data never leave their nodes; only\nalgorithms, predictive models and non-sensitive metadata are exchanged on the\nnetwork. The computations are orchestrated by a Distributed Ledger Technology\nwhich guarantees traceability and authenticity of information without needing\nto trust a third party. Although originally developed for Healthcare\napplications, Substra is not data, algorithm or programming language specific.\nIt supports many types of computation plans including parallel computation plan\ncommonly used in Federated Learning. With appropriate guidelines, it can be\ndeployed for numerous Machine Learning use-cases with data or algorithm\nproviders where trust is limited.\n

Ismael Lastra Martínez, Sreya Francis, Abdelhakim Hafid

Although Federated Learning allows for participants to contribute their local data without it being revealed, it faces issues in data security and in accurately paying participants for quality data contributions. In this paper, we propose an EOS Blockchain design and workflow to establish data security, a novel validation error based metric upon which we qualify gradient uploads for payment, and implement a small example of our blockchain Federated Learning model to analyze its performance.

Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele · 7 authors

Anti-money laundering (AML) regulations play a critical role in safeguarding\nfinancial systems, but bear high costs for institutions and drive financial\nexclusion for those on the socioeconomic and international margins. The advent\nof cryptocurrency has introduced an intriguing paradox: pseudonymity allows\ncriminals to hide in plain sight, but open data gives more power to\ninvestigators and enables the crowdsourcing of forensic analysis. Meanwhile\nadvances in learning algorithms show great promise for the AML toolkit. In this\nworkshop tutorial, we motivate the opportunity to reconcile the cause of safety\nwith that of financial inclusion. We contribute the Elliptic Data Set, a time\nseries graph of over 200K Bitcoin transactions (nodes), 234K directed payment\nflows (edges), and 166 node features, including ones based on non-public data;\nto our knowledge, this is the largest labelled transaction data set publicly\navailable in any cryptocurrency. We share results from a binary classification\ntask predicting illicit transactions using variations of Logistic Regression\n(LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional\nNetworks (GCN), with GCN being of special interest as an emergent new method\nfor capturing relational information. The results show the superiority of\nRandom Forest (RF), but also invite algorithmic work to combine the respective\npowers of RF and graph methods. Lastly, we consider visualization for analysis\nand explainability, which is difficult given the size and dynamism of\nreal-world transaction graphs, and we offer a simple prototype capable of\nnavigating the graph and observing model performance on illicit activity over\ntime. With this tutorial and data set, we hope to a) invite feedback in support\nof our ongoing inquiry, and b) inspire others to work on this societally\nimportant challenge.\n

Anil Gaihre, Santosh Pandey, Hang Liu

The world economy is embracing the next generation currency, i.e., cryptocurrencies, which dates back to 2009 when Satoshi Nakamoto made Bitcoin publicly available. Rooted from the nature of decentralization and anonymity of blockchain, the cryptocurrencies have, unfortunately, been leveraged for illicit activities by the criminals. The good news is that typical cryptocurrencies, such as Bitcoin, have to publicly publish their transactions, known as a graph, to retain their ultimate goal of trustless and decentralized transaction verification, which lends law enforcement a means to deanonymizing cryptocurrencies. At meantime, graph learning is an extremely powerful tool to extract the latent features of each vertex in a graph to fulfill various tasks, such as, classifying graph vertices. In this work, we discuss the promises and challenges of exploiting graph learning to deanonymizing cryptocurrencies, which can aid the cyberfighters to circumvent cryptocurrency-based illicit activities.

Dan Lin, Jiajing Wu, Qi Yuan, Zibin Zheng

Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so it is difficult to accurately describe the detailed characteristics of the transaction networks. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public, and also brings unprecedented opportunities for the transaction network analysis. By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp. Then we define the problem of Temporal Weighted Multidigraph Embedding (T-EDGE) by incorporating both temporal and weighted information of the edges, the purpose being to capture more comprehensive properties of dynamic transaction networks. To evaluate the effectiveness of the proposed embedding method, we conduct experiments of node classification on real-world transaction data collected from Ethereum. Experimental results demonstrate that T-EDGE outperforms baseline embedding methods, indicating that time-dependent walks and multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.

Cedric G. Sanders, Thomas Liebig

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.

Haoxuan Li, Hui Huang, Shichong Tan, Ning Zhang · 6 authors

Reputation evaluation system, as the publisher and analyser of evaluation, is an important influencing factor for users and sellers in online business. Traditional reputation evaluation system always requires a third party to achieve the operation of analysis and publishment. However, a third party often exposes the identity of the users, and leaks the information. As far as we know, all existing revocable reputation evaluations are based on the third-party model. In this paper, we present a new reputation evaluation system based on blockchain. Compared with traditional reputation evaluation systems, our system removes the third party, it allows users to modify their own evaluation information. Moreover, the user's privacy also can be protected. The experiment performance demonstrates that the overhead of the system is acceptable, the system is feasible and efficient.

Alexander Mikroyannidis, Allan Third, John Domingue

Blockchain technology provides a decentralised peer-to-peer infrastructure, supporting openness, transparency, accountability, identity management and trust. As such, the Blockchain has the potential to revolutionise online education in a number of ways. Blockchain technology offers opportunities to thoroughly rethink how we find educational content and training services online, how we register and pay for them, as well as how we get accredited for what we have learned and how this accreditation affects our career trajectory. This paper explores the different aspects of online education that are affected by this new paradigm. In particular, we investigate the different scenarios where the use of Blockchain technology can make online education more open and decentralised, while placing learners in control of their learning process and its associated data. Additionally, we discuss various approaches to the Semantic Blockchain and the applications of these approaches on online education.

Silivanxay Phetsouvanh

Anwitaman Datta for his continuous support during my Ph.D. studies. His patient guidance, encouragement, and immense knowledge are precious to me and beyond what words can express. I really appreciate having a

Marc Jourdan, Sébastien Blandin, Laura Wynter, Pralhad Deshpande

The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain, setting the basis for follow-up AI applications on Bitcoin transactions. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties implied by this model. At the network level, we show evidence of complex transaction-to-transaction behavior and present a relevant discriminative model of the agent categories. Performance of both the block-based graphical model and the network-level discriminative model are evaluated on a subset of the public Bitcoin Blockchain.

Lihao Nan, Dacheng Tao

Bitcoin is a decentralized transaction platform and the largest cryptocurrency system. Bitcoin represents a chain of blocks containing its entire legal transaction history, thereby providing convenience for tracking money. However, mixing services are used as an effective means to hide the identity of a transaction address by combining several transfers from different users. Detecting the original user of a Bitcoin address and the money flow is essential in some special circumstances such as anomaly detection. Recognizing Bitcoin mixing services and de-mixing user accounts have only rarely been studied. Here we demonstrate that Bitcoin transaction graphs possess community properties and that a mixing service can be regarded as a cluster outlier. Motivated by the success of graph embedding in social network analysis, we propose a feature-based method to identify mixing services, testing our method on the real Bitcoin ledger.

Srijan Kumar, Francesca Spezzano, V. S. Subrahmanian, Christos Faloutsos

Weighted signed networks (WSNs) are networks in which edges are labeled with positive and negative weights. WSNs can capture like/dislike, trust/distrust, and other social relationships between people. In this paper, we consider the problem of predicting the weights of edges in such networks. We propose two novel measures of node behavior: the goodness of a node intuitively captures how much this node is liked/trusted by other nodes, while the fairness of a node captures how fair the node is in rating other nodes' likeability or trust level. We provide axioms that these two notions need to satisfy and show that past work does not meet these requirements for WSNs. We provide a mutually recursive definition of these two concepts and prove that they converge to a unique solution in linear time. We use the two measures to predict the edge weight in WSNs. Furthermore, we show that when compared against several individual algorithms from both the signed and unsigned social network literature, our fairness and goodness metrics almost always have the best predictive power. We then use these as features in different multiple regression models and show that we can predict edge weights on 2 Bitcoin WSNs, an Epinions WSN, 2 WSNs derived from Wikipedia, and a WSN derived from Twitter with more accurate results than past work. Moreover, fairness and goodness metrics form the most significant feature for prediction in most (but not all) cases.

A. Kilkerry Neto, Gerson Zaverucha, Luı́s Alfredo Vidal de Carvalho

Pinkas defined (1991, 1992) a bi-directional mapping between propositional logic formulas and energy functions of symmetric neural networks. He showed that determining whether a propositional logic formula is satisfiable is equivalent to finding whether the global minimum of its associated energy function is equal to zero. He also defined how to transform a first-order resolution-based theorem proof of a formula (query Q) from a given set of formulas (knowledge base KB) into a set of constraints described by a set of propositional logic formulas C. Then he showed that the satisfaction of C is sound and complete with respect to a first-order resolution-based proof of Q from KB. Therefore finding that the global minimum of the energy function associated to C is equal to zero is sound and complete with respect to a first-order resolution-based proof of Q from KB. The proof itself could be extracted from the state of the neurons when the network stops in the global minimum. Pinkas did not implement his system. In this work we point out some adjustments to C and we present an implementation of the revised system. We also show some experimental results and point out some future works.