Blockchain Papers

Simon Curty, Hans-Georg Fill, Rafael S. Gonçalves, Mark A. Musen

No abstract is available for this record.

Filippo Ferroni

Most of the volatility is the result of the linkages that amplify and reverberate any price movements in the market.

Liang Cai, Qilei Li, Xiubo Liang

No abstract is available for this record.

Chris Mao

Non-fungible tokens (“NFTs”) are an emerging digital asset that has captured global attention with multi-million-dollar price tags for seemingly basic pixelated JPEG files. In March 2021, British auction house Christie’s sold a digital artwork, ‘Everydays: The First 5,000 Days’, by artist Mike Winkelmann (“Beeple”) for the Ether equivalent of $69.3 million, making it the third-most expensive artwork by a living artist.1 Beeple’s sale was by no means alone—Sotheby's sold an NFT collection of 101 ‘Bored Apes’ for $24.4 million;2 CryptoPunk #7804, one of 10,000 unique ‘CryptoPunk’ NFTs sold for $7.56 million,3 and Twitter founder Jack Dorsey’s first-ever tweet sold for $2.9 million as an NFT.4 NFT sales in the first-half of 2021 have already exceeded $2.5 billion,5 and, as of October 2021, the total value of NFTs on the Ethereum blockchain is estimated to be at least $14.3 billion.6 On one hand, NFTs may be poised to revolutionize creative industries and drastically alter consumer interaction with digital media.7 On the other hand, the NFT market is simultaneously both ripe for speculative investment and vulnerable to criminal activity.8 To date, there appears to be no consensus on the regulation of NFTs, neither from the perspective of generally applicable laws, regulatory capture under existing financial market regulation, nor the implementation of new digital asset laws. This paper attempts to highlight several pertinent dangers of NFTs, from a profound misunderstanding of what an NFT transaction entails, their bubble-like pricing, to various criminal activity concerns. By illustrating how existing laws and regulations may not fully capture nor address these dangers, as well as the potential oversight of NFTs in newly proposed digital asset laws, this paper proposes a categorial approach to regulating NFTs, by reducing the current (and likely future) use-cases of NFTs to their constituent categories and in turn, suggesting the most appropriate regulatory approach to each. Ultimately, given the (potential) wide-ranging use-cases of NFTs, this paper proposes that the NFT’s intended use-case described in broad categorical terms, or more aptly, its underlying reference asset and simultaneous conveyance, or lack thereof, should dictate the regulatory approach.

Eranga Bandara, Deepak K. Tosh, Sachin Shetty, Bheshaj Krishnappa

For operational efficiency, enterprise-level Energy Delivery Systems (EDS) rely on a number of software or hardware providers. Overseas suppliers generally manufacture and integrate critical EDS components, increasing the attack surface for adversaries looking to enter EDS (e.g., the recent SolarWinds supply chain attack). The EDS supply chain requires cyber risk management that can track cyber vulnerabilities, establish quantifiable mechanisms to understand the severity and exploitability of EDS applications while providing a remediation plan to effectively mitigate such risks. In this work, we propose a Cyber Supply Chain Provenance platform for EDS by leveraging distributed ledger technology for enabling cyber risk management capability to defend and respond to cyber supply-chain attacks (e.g., SolarWinds) and establish data provenance in a cyber supply chain ecosystem.

Shlomi Linoy, Suprio Ray, Natalia Stakhanova

The rapid adoption of blockchain technologies and particularly smart contracts has been overshadowed by numerous security concerns. Over the past few years, a number of reports exposed smart contracts vulnerabilities and exploits, which mainly stem from the immaturity of the field, and consequently a lack of knowledge and tools for automated analysis and verification of smart contracts. The restricting properties of the blockchain environment, such as the immutability of deployed contracts, encumber the analysis and mitigation of vulnerabilities and bugs in deployed contracts. To address these challenges, we propose EtherProv, a novel provenance tracking system that leverages static and dynamic analysis synergy to enable detection and mitigation of known security issues in Ethereum smart contracts. EtherProv leverages Solidity source code static and dynamic analysis data through contract bytecode instrumentation. The collected data is transformed into a unified, high-level representation, which can be queried using concise and descriptive Datalog queries. Within the provenance framework, EtherProv is able to analyze contracts' execution flow over time, to detect vulnerabilities within a single contract execution flow and across multiple interacting contracts, and to mitigate new security threats in already deployed contracts. Our evaluation shows that EtherProv can efficiently and precisely identify vulnerable contracts with an average contract instrumentation gas overhead of 18.9%.

Eranga Bandara, Sachin Shetty, Abdul Rahman, Ravi Mukkamala

“Let'sTrace” is a blockchain-enabled cyber supply chain provenance platform. It enables cyber supply chain verification with TUF (The update framework) and In-ToTo frameworks. In this paper, we discuss a prototype where the TUF and In-ToTo frameworks have been integrated into a blockchain smart contract platform to facilitate robust supply chain verification functions. In this paper, we describe the TUF and In-ToTo frameworks and how they are implemented as the secure software update system which can verify the software update and use In-ToTo metadata files as the software supply chain end-to-end verification. Further, we have proposed a mechanism to analyze cyber supply chain information and software updates on different parties using a blockchain-enabled federated learning system. With this approach, we make the cyber supply chain more secure, reliable, and meaningful to conduct further analytics efficiently. This paper aims to demonstrate how through the use of our integrated approach, cyber defenders can harden relevant SSCs thus reducing a multitude of attack vectors leading to a stronger overall security posture.

P. Abhishek, Y. Akash, D. G. Narayan

Cloud computing has become an important technology for the processing of large computationally expensive programs and data from real-world. IT organizations are en-trusting the cloud vendors for the security of their computational infrastructures including the data. Thus, there is a need for better provenance assurance for the data present in the cloud for better security and to establish trust between the cloud vendors and customers. Blockchain technology operates in a decentralized way for building trust between the entities in the system using immutable ledger. In this work, we propose the decentralized and trusted cloud data provenance mechanism using Ethereum blockchain platform, IPFS and scalable consensus mechanism. PoW is the consensus algorithm currently used in Ethereum Blockchain. PoW consensus mechanism needs lot of computational power for the processing of data in the Blockchain. Thus, we implement Proof of Stake (POS) consensus algorithm in Ethereum to improve the efficiency of the proposed data provenance mechanism. We demonstrate the performance of PoS-enabled provenance framework in a multi-node testbed. The results reveal that POS performs better than PoW for cloud data provenance.

Alex Williams, Deepak K. Tosh

Provenance in computing systems is the key to establishing data integrity. It provides a historical ledger of data's life cycle through creation, ownership, consumption, and manipulation. With provenance in hand, it is possible to reverse engineer the state of the data that can lead to understanding how it was derived and verify its accuracy. This need for data integrity is extremely critical in scientific workflows to ensure verifiability and repeatability of the derived results. Due to the vast computational power required by scientific workflows, many operate within high performance computing (HPC) environments, where data is consumed and manipulated by a multitude of processes running on highly distributed infrastructure. The current landscape of HPC environments range from on-premise systems to cloud and grid based solutions. While the majority of research in digital provenance has been focused on standalone HPC environments, provenance in a heterogeneous HPC environment remains a challenge. In this paper we propose HyperProvenance, a high level system architecture especially for next generation heterogeneous HPC environments, which aims to increase confidence in workflow result accuracy through secure provenance collection.

Franck Cassez, Joanne Fuller, Aditya Asgaonkar

Abstract We report our experience in the formal verification of the reference implementation of the Beacon Chain. The Beacon Chain is the backbone component of the new Proof-of-Stake Ethereum 2.0 network: it is in charge of tracking information about the validators , their stakes , their attestations (votes) and if some validators are found to be dishonest, to slash them (they lose some of their stakes). The Beacon Chain is mission-critical and any bug in it could compromise the whole network. The Beacon Chain reference implementation developed by the Ethereum Foundation is written in Python, and provides a detailed operational description of the state machine each Beacon Chain’s network participant (node) must implement. We have formally specified and verified the absence of runtime errors in (a large and critical part of) the Beacon Chain reference implementation using the verification-friendly language Dafny. During the course of this work, we have uncovered several issues, proposed verified fixes. We have also synthesised functional correctness specifications that enable us to provide guarantees beyond runtime errors. Our software artefact with the code and proofs in Dafny is available at https://github.com/ConsenSys/eth2.0-dafny .

Milán Janosov, Flóra Borsi

In this piece, we overview Isaac Asimov's most iconic work, the Foundation series, with two primary goals: to provide quantitative insights about the novels and bridge data science with digital art. First, we rely on data science and text processing tools to describe certain properties of Asimov's career and the novels, focusing on the different worlds in Asimov's universe. Then we transform the books' texts into a network centered around Asimov's planets and their semantic context. Finally, we introduce the world of crypto art and non-fungible tokens (NFTs) by transforming the visualized network into a high-end digital piece of art minted as an NFT. Additionally, to pay tribute to Asimov's devotion to robotics and artificial intelligence, we use OpenAI's Generative Pre-trained Transformer 3 (GPT-3) to draft several paragraphs of this paper.

Megan Mun Li, Tsung-Ting Kuo

BACKGROUND: An image sharing framework is important to support downstream data analysis especially for pandemics like Coronavirus Disease 2019 (COVID-19). Current centralized image sharing frameworks become dysfunctional if any part of the framework fails. Existing decentralized image sharing frameworks do not store the images on the blockchain, thus the data themselves are not highly available, immutable, and provable. Meanwhile, storing images on the blockchain provides availability/immutability/provenance to the images, yet produces challenges such as large-image handling, high viewing latency while viewing images, and software inconsistency while storing/loading images. OBJECTIVE: This study aims to store chest x-ray images using a blockchain-based framework to handle large images, improve viewing latency, and enhance software consistency. BASIC PROCEDURES: We developed a splitting and merging function to handle large images, a feature that allows previewing an image earlier to improve viewing latency, and a smart contract to enhance software consistency. We used 920 publicly available images to evaluate the storing and loading methods through time measurements. MAIN FINDINGS: The blockchain network successfully shares large images up to 18 MB and supports smart contracts to provide code immutability, availability, and provenance. Applying the preview feature successfully shared images 93% faster than sharing images without the preview feature. PRINCIPAL CONCLUSIONS: The findings of this study can guide future studies to generalize our framework to other forms of data to improve sharing and interoperability.

Matea Vasilj, Sven Skender, Mia Jurdana, Marko Horvat

Student non-profit organizations are a specific type of small associations that congregate students from diverse backgrounds around a common purpose. These organizations are non-profit driven, autonomous, and typically have largely similar statutes and regulations. In the context of software engineering, information systems that support their business processes have similar software requirements specifications. DESPRO is a web-based platform that fully supports principal use cases of students' non-profit organizations. The platform has three uses: accounting, voting, and fundraising management. DESPRO allows such organizations to begin with their principal operations practically immediately without unnecessary overhead. Unique features that distinguish DESPRO from other similar systems are: 1) lightweight and streamlined deployment process, 2) minimal required remote maintenance, 3) pre-defined functionalities specifically suited for student non-profit organizations, 4) user anonymity customized to specific functional requirements, 5) voting without prior user registration. The platform was developed with open-source technologies Node.js, Vue.js, Vuetify, Bootstrap, Sequalize, Axios, Web3, MariaDB, and smart contracts for Ethereum. It is freely available for noncommercial use by contacting the first author.

Antonio Tenorio-Fornés, Elena Pérez Tirador, Antonio A. Sánchez‐Ruiz, Samer Hassan

Scientific publication and its Peer Review system strongly rely on a few major industry players controlling most journals (e.g. Elsevier), databases (e.g. Scopus) and metrics (e.g. JCR Impact Factor), while keeping most articles behind paywalls. Critics to such system include concerns about fairness, quality, performance, cost, unpaid labor, transparency, and accuracy of the evaluation process. The Open Access movement has tried to provide free access to the published research articles, but most of the aforementioned issues remain. In such context, decentralized technologies such as blockchain offer an opportunity to experiment with new models for scientific production and dissemination relying on a decentralized infrastructure, aiming to tackle multiple of the current system shortcomings. This paper makes a proposal for an interoperable decentralized system for an open peer review ecosystem, relying on emerging distributed technologies such as blockchain and IPFS. Such system, named “Decentralized Science” (DecSci), aims to enable a decentralized reviewer reputation system, which relies on an Open Access by-design infrastructure, together with transparent governance processes. Two prototypes have been implemented: a proof-of-concept prototype to validate DecSci’s technological feasibility, and a Minimum Viable Product (MVP) prototype co-designed with journal editors. In addition, three evaluations have been carried out: an exploratory survey to assess interest on the issues tackled; two sets of interviews to confirm both the main problems for editors and to validate the MVP prototype; and a cost analysis of the main operations, both execution cost and actual price. Additionally, the paper discusses the multiple interoperability challenges such proposal faces, including an architecture to tackle them. This work finishes with a review of some of the open challenges that this ambitious proposal may face.

Rui Han, Jiang Xiao, Xiaohai Dai, Shijie Zhang · 7 authors

Recent successful pilot studies on blockchains showed significant benefits of data provenance towards improved visibility and authenticity, as well as a reduction of administrative costs. Traditionally, all the parties reside in a single blockchain system, which leads to various single-chain provenance query schemes. However, for the sake of convenience, it has been a trend for different parties to deploy their own blockchain systems separately, which requires cross-chain provenance queries. Unfortunately, state-of-the-art blockchain query schemes suffered from inauthentic transactions and low efficiencies when expanding to adversarial cross-chain commercial deals. To be more specific, query results may be inconsistent and incomplete over multiple blockchains due to the lack of global knowledge on cross-chain dependencies. Moreover, these schemes perform cross-chain provenance queries in sequence, leading to high query latencies. In this paper, we present Vassago, the first multi-chain system that empowers efficient and authenticated provenance queries. Vassago incorporates three innovative designs: 1) it explores the dependencies of cross-chain transactions, 2) it validates the authenticity of cross-chain provenance by recording and querying the dependencies on a shared blockchain, and 3) it improves efficiency by parallelizing query processes. Our experimental results show that Vassago can shorten the query latency by 85.9% and reduce the storage consumption by up to 85.7%, with negligible overhead.

Marcos Alves Vieira, Sérgio T. Carvalho

An Internet of Things (IoT) scenario is a heterogeneous and complex environment, where large volumes of data are constantly generated, manipulated, and transferred between different devices. In this context, some difficulties may arise, such as the correct identification of the devices generating the data, the trustworthiness of these devices and their generated data, detecting abnormal behavior, and controlling access to the data. Data provenance allows maintaining information about the origin of the data, the operations through which this data has undergone, and its processing history, from its creation to its current state. Aiming to provide means to mitigate the mentioned problems, we propose an architecture for data provenance management in IoT environments, enabling different levels of granularity, using a distributed ledger architecture.

Eranga Bandara, Sachin Shetty, Deepak K. Tosh, Xueping Liang

Enterprise-level energy delivery systems (EDSs) depend on different software or hardware vendors to achieve operational efficiency. Critical components of these systems are typically manufactured and integrated by overseas suppliers, which expands the attack surface to adversaries with additional opportunities to infiltrate into EDSs. Due to this reason, the risk management of the EDS supply chain is crucial to ensure that we are knowledgeable about the vulnerabilities in software and hardware components that comprise any critical part, quantifiable risk metrics to assess the severity and exploitability of the attack, and provide remediation solutions that can influence a prioritized mitigation plan. There is a need to realize cyber supply chain risk management for industrial control systems’ hardware, software, and computing and networking services associated with bulk electric system (BES) operations. This article proposes a blockchain-based cyber supply chain provenance platform (“Vind”) for EDSs to realize data provenance in a cyber supply chain ecosystem.

V Valko, Sergii Stirenko, Ihor Babarykin, Yuri Gordienko

In this article we provide a general framework using Ethereum smart contracts to track back the provenance and evolution of deep learning content (DLC) to its original source even if the DLC was edited (e.g. DL models were retrained or/and datasets were updated) by anonymous authors. The main principle behind the solution is that if the DLC can be credibly traced to a trusted or reputable source, the DLC can then be real and authentic. The solution is proposed in the healthcare context and for medical DLC, especially for federated machine learning, but it can be applied to any other form of DLC.

Tran Khanh Dang, Thu Anh Duong

Purpose In the open data context, the shared data could come through many transformation processes, originating from many sources, which exposes the risk of non-authentic data. Moreover, each data set has different properties, shared under various licenses, which means the updated data could change its characteristics and related policies. This paper aims to introduce an effective and elastic solution to keep track of data changes and manage their characteristics within the open data platform. These changes have to be immutable to avoid violated modification and could be used as the certified provenance to improve the quality of data. Design/methodology/approach This paper will propose a pragmatic solution that focuses on the combination of comprehensive knowledge archive network – the broadest used open data platform and hyperledger fabric blockchain to ensure all the changes are immutable and transparent. As using smart contracts plus a standard provenance data format, all processes are running automatically and could be extended to integrate with other provenance systems and so the introduced solution is quite flexible to be used in different open data ecosystems and real-world application domains. Findings The research involves some related studies about the provenance system. This study finds out that most of the studies are focused on the commercial sector or applicable to a specific domain and not relevant for the open-data section. To show that the proposed solution is a logical and feasible direction, this paper conducts an experimental sample to validate the result. The testing model is running successfully with an elastic system architect and promising overall performance. Originality/value Open data is the future of many businesses but still does not receive enough attention from the research community. The paper contributes a novel approach to protect the provenance of open data.

Kevin Wittek, Neslihan Wittek, James H. Lawton, Iryna Dohndorf · 6 authors

The traditional Proof of Existence blockchain service on the Bitcoin network can be used to verify the existence of any research data at a specific point of time, and to validate the data integrity, without revealing its content. Several variants of the blockchain service exist to certify the existence of data relying on cryptographic fingerprinting, thus enabling an efficient verification of the authenticity of such certifications. However, nowadays research data is continuously changing and being modified through different processing steps in most scientific research workflows such that certifications of individual data objects seem to be constantly outdated in this setting. This paper describes how the blockchain and distributed ledger technology can be used to form a new certification model, that captures the research process as a whole in a more meaningful way, including the description of the used data through its different stages and the associated computational pipeline, code for analysis and the experimental design. The scientific blockchain infrastructure bloxberg, together with a deep learning based analysis from the behavioral science field are used to show the applicability of the approach.

Anna Chukhnina, Grigorii Melnikov, Anton Pecherkin, Александр Владимирович Соколов · 5 authors

Many research papers with data processing are released daily. Some of them are prone to statistical errors caused by selective reporting. It results in a lack of reproducibility and wrong conclusions. In the Non-selective reporting demo, we show how to deal with the reporting bias problem through a distributed ledger system.

Khadija Osman, Olga Baysal

Bitcoin is a virtual and decentralized cryptocurrency that operates in a peer-to-peer network providing a private payment mechanism. It is a multi-billion dollar cryptocurrency, and hundreds of other cryptocurrencies are created based on it. Bitcoin is based on open source software (OSS) development. This paper presents the first comprehensive study of the Bitcoin ecosystem in GitHub organized around 481 most popular and actively developed Bitcoin related projects over eight years (2010-2018). Our work includes manual categorization of the projects, defining software health metrics, classification of projects according to these health metrics, and evaluation of the health trends of the ecosystem. The main findings suggest that the Bitcoin ecosystem in GitHub is represented by nine categories of projects. Moreover, the health of the majority of the projects is assessed as “Low Risk”.

Michael F. Marchini

Large-scale development and maintenance projects involve numerous dependencies across multiple disparate organizations, and these projects are subject to contractual stipulations, customer requirements, stakeholder expectations, and compliance/regulatory issues. Because these dependencies and constraints can change over time, mechanisms must exist to absorb, propagate, and implement perturbations to the original scope of work. Such mechanisms are generally human-oriented, involving significant effort to confirm successful modification while avoiding systemic regression. Distributed Ledger Technology alleviates concerns of regression while providing costeffective, actionable business insight during the development of large-scale, integrated systems. Fine-grained insights benefit all parties involved in the system's development, allowing for the incorporation of necessary systemic changes in an economical and informed manner. The system described in this work provides users of varying business roles with a single, clear, and concise view of their collaborative, large-scale development projects. This approach provides needed flexibility and functionality, allowing its users to maintain rigorous traceability while minimizing the time and attention needed to maintain valid project records. Further, practitioners are more well-informed regarding change impact and need not rely solely on subject-matter experts' insights and judgments; instead, they rely on artifact provenance to reliably view the past and present of the project and reliably predict future impacts. Combining distributed ledgers and distributed storage creates a scalable, flexible platform for enterprise-grade project management.

Hyeong Joon Kim, Hye Hyeon Kim, Hosuk Ku, Kyung Don Yoo · 11 authors

BACKGROUND: ) for data privacy and participatory medicine; however, its fully decentralized architecture has come at the expense of decentralized data management and data provenance. OBJECTIVE: The introduction of blockchain and smart contract technologies to the legacy Health Avatar Platform with a clinical metadata registry remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data. METHODS: The Health Avatar Platform decentralizes patient data in appropriate locations (ie, on patients' smartphones and on physicians' smart devices). We implemented an Ethereum-based hash chain for all transactions and smart contract-based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into 3 smart contracts, in this case, a health data transaction manager, a transaction status manager, and an application programming interface transaction manager. The actual decentralized health data are managed in an off-chain manner on appropriate smart devices and authenticated by hashed metadata on-chain. RESULTS: Metadata of each data transaction are captured in a Health Avatar Platform blockchain node by the smart contracts. We provide workflow diagrams each of the 3 use cases of data push (from a physician app or an intelligent agents to a patient Avatar), data pull (request to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash-chained metadata support data element-level verification of data integrity in subsequent transactions. Smart contracts can incentivize transactions for data sharing and intelligent digital health care services. CONCLUSIONS: Health Avatar Platform and interconnected patient Avatars, physician apps, and intelligent agents provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions with smart contracts.