Blockchain Papers

Wulf A. Kaal

No abstract is available for this record.

Bianca Trovò, Nazzareno Massari

Peer-review is a necessary and essential quality control step for scientific publications but lacks proper incentives. Indeed, the process, which is very costly in terms of time and intellectual investment, not only is not remunerated by the journals but is also not openly recognized by the academic community as a relevant scientific output for a researcher. Therefore, scientific dissemination is affected in timeliness, quality, and fairness. Here, to solve this issue, we propose a blockchain-based incentive system that rewards scientists for peer-reviewing other scientists' work and that builds up trust and reputation. We designed a privacy-oriented protocol of smart contracts called Ants-Review that allows authors to issue a bounty for open anonymous peer-reviews on Ethereum. If requirements are met, peer-reviews will be accepted and paid by the approver proportionally to their assessed quality. To promote ethical behavior and inclusiveness the system implements a gamified mechanism that allows the whole community to evaluate the peer-reviews and vote for the best ones.

David F. Ferraiolo, Joanna F. DeFranco, D. Richard Kuhn, Joshua Roberts

Distributed systems have always presented complex challenges, and technology trends are in many ways making the software designer's job more difficult. In particular, today's systems must successfully handle.

Kevin Wittek, Dominik Krakau, Neslihan Wittek, James H. Lawton · 5 authors

Proof of Existence as a blockchain service has first been published in 2013 as a public notary service on the Bitcoin network and can be used to verify the existence of a particular file in a specific point of time without sharing the file or its content itself. This service is also available on the Ethereum based bloxberg network, a decentralized research infrastructure that is governed, operated and developed by an international consortium of research facilities. Since it is desirable to integrate the creation of this proof tightly into the research workflow, namely the acquisition and processing of research data, we show a simple to integrate MATLAB extension based solution with the concept being applicable to other programming languages and environments as well.

Pingcheng Ruan, Tien Tuan Anh Dinh, Qian Lin, Meihui Zhang · 6 authors

The success of Bitcoin and other cryptocurrencies bring enormous interest to blockchains. A blockchain system implements a tamper-evident ledger for recording transactions that modify some global states. The system captures the entire evolution history of the states. The management of that history, also known as data provenance or lineage, has been studied extensively in database systems. However, querying data history in existing blockchains can only be done by replaying all transactions. This approach is feasible for large-scale, offline analysis, but is not suitable for online transaction processing. We present LineageChain, a fine-grained, secure, and efficient provenance system for blockchains. LineageChain exposes provenance information to smart contracts via simple interfaces, thereby enabling a new class of blockchain applications whose execution logics depend on provenance information at runtime. LineageChain captures provenance during contract execution and stores it in a Merkle tree. LineageChain provides a novel skip list index that supports efficient provenance queries. We have implemented LineageChain on top of Hyperledger Fabric and a blockchainoptimized storage system called ForkBase. We conduct extensive evaluation, demonstrating benefits of LineageChain, its efficient querying, and its small storage overhead.

Gracie Carter, Ben Chevellereau, Hossain Shahriar, Sweta Sneha

The healthcare system in the United States is unique. From payor to provider, patients have the freedom of choice. This creates a complicated and profitable paradigm of care. Legislation defines government expectations of data exchange; however, the methods are left to the discretion of the stakeholders. Today, devices and programs are not built to unified standards, thus they do not share data easily. This communication between software is known as interoperability. We address the health data interoperability by leveraging Fast Health Interoperable Resource (FHIR) standard, a viewer of FHIR called OpenPharma, and Blockchain technology. Our proof of concept, called "OpenPharma Blockchain on FHIR" (OBF), is interoperable by design and grants clinicians access to patient records using a combination of data standards, distributed applications, patient-driven identity management, and the Ethereum blockchain. OBF is a trustless, secure, decentralized, and vendor-independent method for information exchange. It is easy to implement and places the control of records with the patients.

Marten Sigwart, Michael Borkowski, Marco Peise, Stefan Schulte · 5 authors

Abstract As data collected and provided by Internet of Things (IoT) devices power an ever-growing number of applications and services, it is crucial that this data can be trusted. Data provenance solutions combined with blockchain technology are one way to make data more trustworthy by providing tamper-proof information about the origin and history of data records. However, current blockchain-based solutions for data provenance fail to take the heterogeneous nature of IoT applications and their data into account. In this work, we identify functional and non-functional requirements for a secure and extensible IoT data provenance framework, and conceptualise the framework as a layered architecture. Evaluating the framework using a proof-of-concept implementation based on Ethereum smart contracts, we conclude that our framework can be used to realise data provenance concepts for a wide range of IoT use cases. While blockchain technology generally poses constraints on scalability and privacy, we discuss multiple solutions aiming to overcome these issues.

Andrea Margheri, Massimiliano Masi, Abdallah Miladi, Vladimiro Sassone · 5 authors

No abstract is available for this record.

Philipp Lüthi, Thibault Gagnaux, Marcel Gygli

High availability of data is responsible for the current trends in Artificial\nIntelligence (AI) and Machine Learning (ML). However, high-grade datasets are\nreluctantly shared between actors because of lacking trust and fear of losing\ncontrol. Provenance tracing systems are a possible measure to build trust by\nimproving transparency. Especially the tracing of AI assets along complete AI\nvalue chains bears various challenges such as trust, privacy, confidentiality,\ntraceability, and fair remuneration. In this paper we design a graph-based\nprovenance model for AI assets and their relations within an AI value chain.\nMoreover, we propose a protocol to exchange AI assets securely to selected\nparties. The provenance model and exchange protocol are then combined and\nimplemented as a smart contract on a permission-less blockchain. We show how\nthe smart contract enables the tracing of AI assets in an existing industry use\ncase while solving all challenges. Consequently, our smart contract helps to\nincrease traceability and transparency, encourages trust between actors and\nthus fosters collaboration between them.\n

Amrita Jyoti, R. K. Chauhan

Before the invention of the various technologies, managing various activities and actions over the internet was achieved through a centralized server to guarantee valid data. With the expanding measure of accessible storage space and the quickening of data stream incited by the internet, a developing enthusiasm for data about the creation procedure and sources of information has developed. While this wide scope of use territories would profit by provenance data, the kind of provenance information, manipulation and querying facilities required vary from application to application. In this way, to discover the distinctions and similitudes between the different application and information model provenance needs and present a general plan for the arrangement of provenance. By characterizing this plan and applying it to existing work we plan to uncover open inquiries in the region of information provenance In this paper we survey the blockchain and provenance of the data in rice supplychain. We implement our proposed approach using smart contract which is to be deploy on ethereum blockchain network in rice supplychain to show the security and provenance of the data . We can track the progress of rice batch after each stage in blockchain and also discussed the need of provenance of assets in supplychain as it increase the trust of the customer

Mustafa Safa Özdayi, Murat Kantarcıoğlu, Bradley Malin

BACKGROUND: Blockchain has emerged as a decentralized and distributed framework that enables tamper-resilience and, thus, practical immutability for stored data. This immutability property is important in scenarios where auditability is desired, such as in maintaining access logs for sensitive healthcare and biomedical data. However, the underlying data structure of blockchain, by default, does not provide capabilities to efficiently query the stored data. In this investigation, we show that it is possible to efficiently run complex audit queries over the access log data stored on blockchains by using additional key-value stores. This paper specifically reports on the approach we designed for the blockchain track of iDASH Privacy & Security Workshop 2018 competition. In this track, participants were asked to devise an efficient way to run conjunctive equality and range queries on a genomic dataset access log trail after storing it in a permissioned blockchain network consisting of 4 identical nodes, each representing a different site, created with the Multichain platform. METHODS: Multichain duplicates and indexes blockchain data locally at each node in a key-value store to support retrieval requests at a later point in time. To efficiently leverage the key-value storage mechanism, we applied various techniques and optimizations, such as bucketization, simple data duplication and batch loading by accounting for the required query types of the competition and the interface provided by Multichain. Particularly, we implemented our solution and compared its loading and query-response performance with SQLite, a commonly used relational database, using the data provided by the iDASH 2018 organizers. RESULTS: Depending on the query type and the data size, the run time difference between blockchain based query-response and SQLite based query-response ranged from 0.2 seconds to 6 seconds. A deeper inspection revealed that range queries were the bottleneck of our solution which, nevertheless, scales up linearly. CONCLUSIONS: This investigation demonstrates that blockchain-based systems can provide reasonable query-response times to complex queries even if they only use simple key-value stores to manage their data. Consequently, we show that blockchains may be useful for maintaining data with auditability and immutability requirements across multiple sites.

Qirun Wang, Fujian Zhu, Sai Ji, Yongjun Ren

At present, the provenance of electronic records is stored centrally. The centralized way of information storage has huge risks. Whether the database itself is destroyed or the communication between the central database and the external interruption occurs, the provenance information of the stored electronic records will not play its role. At the same time, uncertainties such as fires and earthquakes will also pose a potential threat to centralized databases. Moreover, the existing security provenance model is not specifically designed for electronic records. In this paper, a security provenance model of electronic records is constructed based on PREMIS and METS. Firstly, this paper analyses the security requirements of the provenance information of electronic records. Then, based on the characteristics of blockchain decentralization, and combined with coding theory, a distributed secure provenance guarantees technology of electronic records is constructed, which ensures the authenticity, integrity, confidentiality and reliability of the provenance information.

Magnus W. D. Hanson‐Heine, Alexander P. Ashmore

Blockchain technology has had a substantial impact across multiple disciplines, creating new methods for storing and processing data with improved transparency, immutability, and reproducibility. These developments come at a time when the reproducibility of many scientific findings has been called into question, including computational studies. Here we present a computational chemistry simulation run directly on a blockchain virtual machine, using a harmonic potential to model the vibration of carbon monoxide. The results demonstrate for the first time that computational science calculations are feasible entirely within a blockchain environment and that they can be used to increase transparency and accessibility across the computational sciences.

Muhammad Shoaib Siddiqui, Toqeer Ali, Adnan Nadeem, Waqas Nawaz · 5 authors

Data tracking is of great significance and a central part in digital forensics. In today's complex network design, Internet of Things (IoT) devices communicate with each other and require strong security mechanisms. In maintaining an audit trail of IoT devices or provenance of IoT device data, it is important to know the origins of requests to ensure certain level of trust in IoT data. Blockchain can provide traceability of records generated from IoT devices in a sensitive environment. In this paper, we present an application layer data provenance model that works on execute-order architecture for cloud based IoT networks. It supports high throughput of transactions on the blockchain network with lightweight security overhead by using outsourced encryption on edge nodes. All communications among the IoT devices are connected to a blockchain network and stored on permissioned blockchain peers. The proposed system is evaluated to have less cryptographic load by offloading the IoT nodes with Edge nodes.

Ramon Boss, Kai Brünnler, Anna Doukmak

We try to verify properties of the Bitcoin-S library, a Scala implementation of parts of the Bitcoin protocol. We use the Stainless verifier which supports programs in a fragment of Scala called Pure Scala. Since Bitcoin-S is not written in this fragment, we extract the relevant code from it and rewrite it until we arrive at code that we successfully verify. In that process we find and fix two bugs in Bitcoin-S.

Raisul Rashu

Cryptocurrency development has continuous escalation in the past years and holds its presence significantly in open source development. Online collaborative software development platforms such as GitHub offer us an opportunity to observe developer effort, activity and software growth. Cryptocurrency has enabled various applications such as smart contracts, electronically decentralized payments, etc. Since, prices of each cryptocurrency are driven by many factors, we are interested in investigating how various characteristics of cryptocurrency's codebase development affect market capitalization price. Thus, we conduct a study on a panel dataset containing nearly a year of daily observations of development activity, popularity, and market capitalization for over two hundred open source cryptocurrencies.

Philipp Lüthi, Thibault Gagnaux, Marcel Gygli

High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them.

Emanuele Bellini

Abstract A stable reference of Internet resources is crucial not only to identify a resource in a trustworthy and certified way but also to guarantee continuous access to it over time. The current practice in scientific publication as the use of a Persistent Identifier (PID) like a DOI or Handle, is becoming attractive also for the datasets. In fact, in the era of Big Data, the aspects of replicability and verification of the scientific result are paramount. In this paper we verify the functional feasibility of permissioned blockchain technology as a tool to implement a Trustworthy Persistent Identifier (T-PID) system for datasets in the scientific domain.

Stephan Leible, Steffen Schlager, Moritz Schubotz, Béla Gipp

Many sectors, like finance, medicine, manufacturing, and education, use blockchain applications to profit from the unique bundle of characteristics of this technology. Blockchain technology (BT) promises benefits in trustability, collaboration, organization, identification, credibility, and transparency. In this paper, we conduct an analysis in which we show how open science can benefit from this technology and its properties. For this, we determined the requirements of an open science ecosystem and compared them with the characteristics of BT to prove that the technology suits as an infrastructure. We also review literature and promising blockchain-based projects for open science to describe the current research situation. To this end, we examine the projects in particular for their relevance and contribution to open science and categorize them afterwards according to their primary purpose. Several of them already provide functionalities that can have a positive impact on current research workflows. So, BT offers promising possibilities for its use in science, but why is it then not used on a large-scale in that area? To answer this question, we point out various shortcomings, challenges, unanswered questions, and research potentials that we found in the literature and identified during our analysis. These topics shall serve as starting points for future research to foster the BT for open science and beyond, especially in the long-term.

Mélanie Clément‐Fontaine, Roberto Di Cosmo, Bastien Guerry, Patrick Moreau · 5 authors

Software is a hybrid object in the world research as it is equally a driving force (as a tool), a result (as proof of the existence of a solution) and an object of study (as an artefact). This specific status means we need to define strategies, tools and procedures which are adapted to the various issues it raises. These include the citation of contributions to software design and production, the reproducibility of research results involving software and the wider usage and long-term sustainability of the software heritage created. This opportunity note by the Committee for Open Science's Free Software and Open Source Project Group describes the issues at stake and formulates actionable recommendations.

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

Evan Brinckman, Andrey Kuehlkamp, Jarek Nabrzyski, Ian Taylor

As the public Ethereum network surpasses half a billion transactions and enterprise Blockchain systems becoming highly capable of meeting the demands of global deployments, production Blockchain applications are fast becoming commonplace across a diverse range of business and scientific verticals. In this paper, we reflect on work we have been conducting recently surrounding the ingestion, retrieval and analysis of Blockchain data. We describe the scaling and semantic challenges when extracting Blockchain data in a way that preserves the original metadata of each transaction by cross referencing the Smart Contract interface with the on-chain data. We then discuss a scientific use case in the area of Scientific workflows by describing how we can harvest data from tasks and dependencies in a generic way. We then discuss how crawled public blockchain data can be analyzed using two unsupervised machine learning algorithms, which are designed to identify outlier accounts or smart contracts in the system. We compare and contrast the two machine learning methods and cross correlate with public Websites to illustrate the effectiveness such approaches.

Navya Gouru, Nagalakshmi Vadlamani

The potentiality of Blockchain technology is widespread and applied to diverse fields. Blockchain is a distributed ledger of transactions that store immutable records in chronological order in an append-only mode. Hence, humongous data is stored on the blockchain and will continuously expand over time. Blockchain has been rapidly adopted by many businesses for storing the provenance data because of its salient features like immutability, robustness and tamperproof. Blockchain stores data provenance as transactions that are collected from sources like a centralized cloud or decentralized cloud that helps in identifying cybercrimes. This paper emphasizes on the different approaches of querying the data provenance transactions stored in Ethereum Blockchain based on various search parameters using REST API web services. The approach not only queries based on the first-class data elements like blocks, transactions, account address and contract address but also queries based on the provenance data stored on the Ethereum Blockchain explained with a use case LegalProv.

Nadine Havelange, Michel Dumontier, Birgit Wouters, Jona Linde · 7 authors

In this paper we present our preliminary work on monitoring data License accoUntability and CompliancE (LUCE). LUCE is a blockchain platform solution designed to stimulate data sharing and reuse, by facilitating compliance with licensing terms. The platform enables data accountability by recording the use of data and their purpose on a blockchain-supported platform. LUCE allows for individual data to be rectified and erased. In doing so LUCE can ensure subjects' General Data Protection Regulation's (GDPR) rights to access, rectification and erasure. Our contribution is to provide a distributed solution for the automatic management of data accountability and their license terms.