Bitcoin is the most well-known cryptocurrency. It was first released in 2009 by Satoshi Nakamoto. Bitcoin serves as a decentralized medium of digital exchange, with transactions verified and recorded in the blockchain. The latter is a public immutable distributed ledger that operates without the need of a trusted record keeping authority or a central intermediary. It provides OLTP capabilities with both atomic transactions and data durability guarantees for blockchain transactions. Blockchain ledgers were not designed to perform analytics questions. The availability of the entire bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analyzing the transactions to obtain insights on users/entities patterns and transactions patterns. For these purposes, the authors need to store and analyze cryptocurrency transactions in a data warehouse. In this chapter, they investigate public blockchain datasets, and they overview different data models for setting up a data warehouse appliance of cryptocurrencies.
Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. \nToday, we count more than 10,000 datasets made available online following Semantic Web standards. \nA major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. \nThe main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. \nFirst, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. \nIn particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. \nMany applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. \nHowever, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. \nThus, we introduce a distributed approach of quality assessment of large RDF datasets. \nIt is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. \nBased on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. \nAs a result, it has become difficult to efficiently process these large RDF datasets. \nIndeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. \nTherefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. \nWe conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. \nMore importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. \nThe empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. \nAll the proposed approaches during this thesis are integrated into the larger SANSA framework.
To prove the graph relations such as the connectivity and the isolation for a certified graph, the system of graph signature and proofs have been proposed. In this system, an issuer generates a signature certifying the topology of an undirected graph, and issues a prover the signature. The prover can prove the knowledge of the signature and the graph in the zero-knowledge, i.e., the signature and the signed graph are hidden. In addition, the prover can prove relations on the certified graph such as the connectivity and isolation between two vertexes. In the previous system, using integer commitments on RSA modulus, the graph relations are proved. However, the RSA modulus needs a longer size of each element. Furthermore, the proof size and the verification cost depend on the total numbers of vertexes and edges. In this paper, we propose a graph signature and proof system, where these are computed on bilinear groups without the RSA modulus. Moreover, using a bilinear map accumulator, the prover can prove the connectivity and isolation on a graph, where the proof size and verification cost become independent from the total numbers of vertexes and edges.
Victor Ermolaev, Indrek Klangberg, Yash Madhwal, Silver Vapper · 6 authors
In modern and interconnected world, information is accumulatively stored digitally, making the process of exchanging, gathering and querying the information much easier. Continuously, it has introduced new challenges about how to ensure its consistency and reliability due to the sheer volume of data. A blockchain-based information system can provide an incorruptible record of history, enabling better auditing and data management practices. The paper describes how to combine an Exonum blockchain and a Neo4j graph database into a system that can provide a verifiable audit trail of data integrity and its modifications for information stored in a graph database.
In the Data Technology (DT) era, data have become the most important means of production. With the rapid development of cloud computing, the database technology has become the most basic data technology facility. Especially, the Distributed Databases have been widely used to store and process large-scale data, which are applied in different scenarios than relational databases. In addition, blockchain technology has led to changes in decentralized architecture. The BigchainDB database is based on a distributed database with the features of blockchain. It has not only the advantages of distributed database, such as high throughput, low latency and easy expansion, but also has the characteristics of blockchain, such as decentralized control, tamper-proof and traceability. This paper mainly analyzes the comparison between the BigchainDB and other distributed databases in terms of execution performance. In practice, this paper focuses on the characteristics of BigchainDB, then compares the performance of creating and querying transactions between the BigchainDB and two important distributed databases: HadoopDB and Hive. The experiments in this paper show three things. Firstly, the BigchainDB is obviously better than HadoopDB and Hive in transaction creation. Secondly, the transaction query performance of the BigchainDB is one order higher than them. Thirdly, the BigchainDB is three orders of magnitude higher than them in fuzzy query performance. These results demonstrate that BigchainDB is significantly better than HadoopDB and Hive in both the performance of read and write.
Aman Sharma, Ankit Agrawal, Ashutosh Bhatia, Kamlesh Tiwari
Bitcoin is the most popular cryptocurrency used worldwide. It provides pseudonymity to its users by establishing identity using public keys as transaction end-points. These transactions are recorded on an immutable public ledger called Blockchain which is an append-only data structure. The popularity of Bitcoin has increased unreasonably. The general trend shows a positive response from the common masses indicating an increase in trust and privacy concerns which makes an interesting use case from the analysis point of view. Moreover, since the blockchain is publicly available and up-to-date, any analysis would provide a live insight into the usage patterns which ultimately would be useful for making a number of inferences by law-enforcement agencies, economists, tech-enthusiasts, etc. In this paper, we study various applications and techniques of performing data analytics over Bitcoin blockchain from a graph theoretic perspective. We also propose a framework for performing such data analytics and explored a couple of use cases using the proposed framework.
Blockchain as one of the most well-known examples of Distributed Ledger Technology, has been hailed across the industry as the key to future success within the financial services industry. Blockchain is a platform that supports multiple use cases within Financial Services, and it has the ability to transform a number of processes, including peer-to-peer payments, trade settlements and supply chain tracking. All these use cases depend on one thing that's the Identification and Verification of participant or users. That is called as Know Your Customer(KYC). Verification of User is one of the basic thing that establishes trust between the participants before making any of the transactions. Ever-increasing anti-money laundering (AML) regulations, coupled with other data protection regulations have led many financial institutions to implement lengthy, expensive processes in order to remain compliant. This paper provides solution to this problem using Hyperledger Fabric, which is framework under blockchain technology, provides some key features like permissioned network, confidentiality of Data, and Programmable. These features establish trust, transparency and accountability.
Blockchain is an underlying technology that initially implemented for Bitcoin. Data consistency, decentralization, and non-tamperability of this technology provide unique proposition values in many applications. However, slow transaction speed, high demand of resources, and scalability have become the bottlenecks that limit the blockchain's applicability. Instead of a chain, IOTA is a network of tangle distributed ledger in order to offer higher scalability and quicker transaction time, compared to blockchain. In this paper, we propose a new TSGP (Tree Solve Graph Problem) tip selection algorithm for IOTA. Our experiments show that our algorithm performs better than tip selection algorithms in the literature. Moreover, increasing the number of transactions plays no impact on the transaction connection speed in our algorithm.
\n Current permissioned blockchain systems utilize the key-value data\n model to store and query the ledger. As the key-value pairs are not\n sufficiently expressive to represent relationships between data, we\n present a proposal for the utilization of triples as the data model\n for blockchain systems. This approach enables a powerful query\n engine and reduces the number of data stores that have to be\n maintained.\n
One of the most important components in a public blockchain like Bitcoin and Ethereum is the authenticated data structure that keeps track of all block data, transactions, and the world state (account balance, smart contract states, etc.) Thanks to authenticated data structures, lightweight nodes only need to store authentication information and can delegate queries to those nodes with a full replica of data and the authenticated data structure. The lightweight nodes can trust the query results after verifying against the authentication information. It is also critical to have enough nodes in the network that are equipped with the authenticated data structure to ensure scalability and availability, which is especially important for public blockchains. Therefore, every public blockchain highly encourages users to download the authenticated data structure as the first step.\n\nFetching all elements from the entire authenticated data structure is a novel query type that has not gathered attention in the past. We describe this new emerging query type in the three-party authenticated data structure (ADS). We improve the design and implementation of the authenticated data structure so that the new query type is well-supported. We specifically apply the improvements to the Ethereum blockchain network. With our proposed ADS system in Ethereum, we improve Ethereum state synchronization performance by 216 times.
In this paper, we revisit the fundamental question of Bitcoins security against selfish-mine attack introduced by I. Eyal and E. G. Sirer in We study the state machine of Bitcoin's network under the influence of one pool miner adopting the selfish mine strategy while the rest of the community following the standard protocol. We prove that the process following by the states of Bitcoin's system is a irreducible, positive-recurrent, aperiodic, and discrete Markov chain. We give an invariant (stationary) distribution for this Markov chain and deduce easily the rate of convergence towards the stationary equilibrium situation.
Blockchain is a technology to exchange digital assets including cryptocurrencies, data, software, patents etc. Blockchain is designed in various types depending on different architectures and configurations for different purposes. The mathematical and cryptographic mechanisms and consensus algorithms, implemented in the Blockchain, guarantee trust, security, integrity and availability of transactions between participants. \nThis thesis is aimed to consider mathematical foundation and algorithmic aspects of Ethereum from the base network to high level of its functionality with the comparison to mathematical mechanisms of Bitcoin. In this thesis we describe Ethereum as stack of layers. Our mission is to extract cryptographic and mathematical mechanisms implemented in each layer. \nWe present how hash function is utilized to construct cost function and Hashcash protocol. Hashcash protocol is implemented in Proof of Work (PoW) mechanism. Also, we explain how hash function is used to connect blocks in the chain of blocks and how hash function is used to organize transactions in a Merkle tree and in a Merkle-Patricia-trie. \nThis thesis describes how Elliptic Curve Digital Signature Algorithm (ECDSA) and Lamport signing algorithm provide integrity of transactions. Elliptic Curve Integrated Encryption Scheme (ECIES) is hybrid encryption scheme which is utilized to provide confidentiality of transactions. The Elliptic Curve Cryptography (ECC) is used to disguise identities in both Bitcoin and Ethereum network. The new approaches to provide privacy are Ring signatures and Zk-Snarks. \nThis thesis also explains Bloom filter structure in Ethereum. The Bloom filter is a probabilistic data structure to test membership of elements in big data bases.
Distributed ledger technology, a method of storing and maintaining the integrity of multiple copies of critical data using a massively redundant network of participating machines, has found a “killer application” in blockchain, a type of distributed ledger. A blockchain consists of sequential blocks that may never be modified or reordered, leaving a public, auditable record that is consistent and highly resistant to tampering and deletion. These qualities make blockchain eminently suitable for its most common use, cryptocurrency, and its occasional variants in the form of cryptocurrency tokens, used to represent ownership or some other right to virtual or physical goods and capabilities. Blockchain also enables smart contracts, discrete bodies of software written to serve both as the memorial and the means of execution of an agreement between parties. Smart contracts can have all the elements of a traditional contract, and as jurisdictions legislate or jurists rule on the fine points of enforceability and the acceptability of smart contracts as traditional contracts, applications in nearly every area of commerce have emerged. Digital lawyers may not need to become software developers, but deepening their understanding of the capabilities and limitations of the technology, developing a keen awareness of the issues at the intersection between code and the law, as well as the law’s readiness in this area, will be of great advantage to them and their clients in this rapidly evolving area at the intersection of technology, commerce and law.
Damiano Di Francesco Maesa, Andrea Marino, Laura Ricci
BITCOIN is a novel decentralized cryptocurrency system which has recently received a great attention from a wider audience. An interesting and unique feature of this system is that the complete list of all the transactions occurred from its inception is publicly available. This enables the investigation of funds movements to uncover interesting properties of the BITCOIN economy. In this paper we present a set of analyses of the user graph, i.e. the graph obtained by an heuristic clustering of the graph of BITCOIN transactions. Our analyses consider an up-to-date BITCOIN blockchain, as in December 2015, after the exponential explosion of the number of transactions occurred in the last two years. The set of analyses we defined includes, among others, the analysis of the time evolution of BITCOIN network, the verification of the "rich get richer" conjecture and the detection of the nodes which are critical for the network connectivity.
SILVEIRA, Alexandre Marques Albano da. Prova de conhecimento nulo baseada em isomorfismo de subgrafos. 2016. 71 f. - Dissertação - Universidade Federal do Ceará, Programa de Pós-Graduação em Engenharia Elétrica e da Computação, Sobral, 2016.
Ayush Dubey, Greg D. Hill, Robert Escriva, Emin Gün Sirer
Graph databases have become an increasingly common infrastructure component. Yet existing systems either operate on offline snapshots, provide weak consistency guarantees, or use expensive concurrency control techniques that limit performance. In this paper, we introduce a new distributed graph database, called Weaver, which enables efficient, transactional graph analyses as well as strictly serializable ACID transactions on dynamic graphs. The key insight that allows Weaver to combine strict serializability with horizontal scalability and high performance is a novel request ordering mechanism called refinable timestamps. This technique couples coarse-grained vector timestamps with a fine-grained timeline oracle to pay the overhead of strong consistency only when needed. Experiments show that Weaver enables a Bitcoin blockchain explorer that is 8x faster than Blockchain.info, and achieves 12x higher throughput than the Titan graph database on social network workloads and 4x lower latency than GraphLab on offline graph traversal workloads.