Blockchain Papers

David-Olivier Jaquet-Chiffelle, Eoghan Casey, Jonathan Bourquenoud

No abstract is available for this record.

Dongxiao Liu, Jianbing Ni, Cheng Huang, Xiaodong Lin · 5 authors

Network provenance is essential for Internet-of-Things (IoT) network administrators to conduct the network diagnostics and identify root causes of network errors. However, the distributed nature of the IoT network results in the management of the provenance data at different trust domains, which poses concerns on the security and trustworthiness of the cross-domain network diagnostics. In this article, we propose a blockchain-based architecture for secure and efficient distributed network provenance (SEDNP) in the IoT. Instead of directly storing and querying the whole provenance data on the blockchain with prohibitive implementation cost, we introduce a unified provenance query model and develop a provenance digest strategy that: 1) enables compact (constant size) on-blockchain digests of provenance data and a multilevel index regardless of provenance data volume and 2) ensures the correctness and integrity of provenance query results through the verification of the on-blockchain digests. We formally define the security requirements as Archiving Security along with thorough security analysis. Moreover, we conduct extensive experiments with the integration of a verifiable computation (VC) framework and a blockchain testing network. The experimental results are provided as performance benchmarks to demonstrate the application feasibility of SEDNP.

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.

Changhao Chenli, Taeho Jung

No abstract is available for this record.

Carl Worley, Lu Yu, Richard R. Brooks, Jon Oakley · 8 authors

No abstract is available for this record.

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.

Rui Carreira, Pedro Pinto, António Pinto

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 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.

Zhanghui Liu, Zhihao Huang, Xing Chen, Yan Chen

No abstract is available for this record.

Dinuni Fernando, Siddharth Kulshrestha, J. Dinal Herath, Nitin Mahadik · 9 authors

Modern scientific workflow systems lack strong support for protecting the scientific data and their provenance from being forged or altered. As a result, scientists may be misled into believing that they have found a specific result, but only to discover later that the data they used have been altered and should not be trusted. To address this limitation, we develop a new system called SciBlock that leverages recent advances in blockchain technology to provide a tamper-proof and non-repudiable storage for scientific workflow provenance. SciBlock provides primitives that allow users to query scientific workflow provenance data efficiently. Moreover, SciBlock offers the capability of invalidating wrong or outdated scientific workflow provenance data without removing them from the blockchain. We conducted extensive experiments to evaluate the performance and scalability of SciBlock. Our experimental results show that SciBlock offers a promising approach to enhancing scientific research integrity in a distributed collaborative environment.

R. P. Jagadeesh Chandra Bose, Kanchanjot Kaur Phokela, Vikrant Kaulgud, Sanjay Podder

There has been a considerable shift in the way how software is built and delivered today. Most deployed software systems in modern times are created by (autonomous) distributed teams in heterogeneous environments making use of many artifacts, such as externally developed libraries, drawn from a variety of disparate sources. Stakeholders such as developers, managers, and clients across the software delivery value chain are interested in gaining insights such as how and why an artifact came to where it is, what other artifacts are related to it, and who else is using this. Software provenance encompasses the origins of artifacts, their evolution, and usage and is critical for comprehending, managing, decision-making, and analyzing software quality, processes, people, issues etc. In this paper, we propose an extensible framework based on standard provenance model specifications and blockchain technology for capturing, storing, exploring, and analyzing software provenance data. Our framework (i) enhances trustworthiness of provenance data (ii) uncovers non-trivial insights through inferences and reasoning, and (iii) enables interactive visualization of provenance insights. We demonstrate the utility of the proposed framework using open source project data.

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.

Ah Ra Lee, Min Kim, Il Kon Kim

The amount of healthcare data is increasing. However, issues with unstandardized data formats and the reliability of provenance make the data challenging to share with other institutions. This paper proposes a standards-based sharing framework, SHAREChain, which incorporates two features to deal with reliability and interoperability issues. First, it improves reliability by using the data integrity of a Blockchain-registry and constitutes a Consortium Blockchain Network to share data solely between authenticated institutions. The second feature improves interoperability with standards relating to healthcare data sharing: Fast Healthcare Interoperability Resources and Cross-Enterprise Document Sharing.

Kapil Singi, Jagadeesh Chandra Bose R P, Sanjay Podder, Adam P. Burden

Modern software delivery happens in a geographically distributed environment and resembles like a supply chain - consists of various participants, involves various phases, needs adherence to multiple regulations and needs to maintain artifacts' integrity throughout the delivery phases. This shift in software development brings along with it several challenges ranging from communication of information/knowledge, coordination and control of teams, activities adhering to goals and policies and artifacts adhering to quality, visibility, and management. With the dispersion of centralized control over software delivery to autonomous delivery organizations, the variety of processes and tools used turns transparency into opacity as autonomous teams use different software processes, tools, and metrics, leading to issues like ineffective compliance monitoring, friction prone coordination, and lack of provenance, and thereby trust. In this paper, we present a delivery governance framework based on distributed ledger technology that uses a notion of `software telemetry' to record data from disparate delivery partners and enables compliance monitoring and adherence, provenance and traceability, transparency, and thereby trust.

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.