Richard Blythman, Mohamed Arshath, Jakub Smékal, Hithesh Shaji · 6 authors
AI requires heavy amounts of storage and compute. As a result, AI developers are regular users of centralised cloud services such as AWS, GCP and Azure, compute environments such as Jupyter and Colab notebooks, and AI Hubs such as HuggingFace and ActiveLoop. There services are associated with certain benefits and limitations that stem from the underlying infrastructure and governance systems with which they are built. These limitations include high costs, lack of monetization and reward, lack of control and difficulty of reproducibility. At the same time, there are few libraries that allow data scientists to interact with decentralised storage in the language that data scientists are used to, and few hubs where they can discover and interact with AI assets. In this report, we explore the potential of decentralized technologies - such as Web3 wallets, peer-to-peer marketplaces, decentralized storage (IPFS and Filecoin) and compute, and DAOs - to address some of the above limitations. We showcase some of the libraries and integrations that we have built to tackle these issues, as well as a proof of concept of a decentralized AI Hub app, that all use IPFS as a core infrastructural component.
Daniël Reijsbergen, Aung Htein Maw, Zheng Yang, Tien Tuan Anh Dinh · 5 authors
Users today expect more security from services that handle their data. In addition to traditional data privacy and integrity requirements, they expect transparency, i.e., that the service's processing of the data is verifiable by users and trusted auditors. Our goal is to build a multi-user system that provides data privacy, integrity, and transparency for a large number of operations, while achieving practical performance. To this end, we first identify the limitations of existing approaches that use authenticated data structures. We find that they fall into two categories: 1) those that hide each user's data from other users, but have a limited range of verifiable operations (e.g., CONIKS, Merkle2, and Proofs of Liabilities), and 2) those that support a wide range of verifiable operations, but make all data publicly visible (e.g., IntegriDB and FalconDB). We then present TAP to address the above limitations. The key component of TAP is a novel tree data structure that supports efficient result verification, and relies on independent audits that use zero-knowledge range proofs to show that the tree is constructed correctly without revealing user data. TAP supports a broad range of verifiable operations, including quantiles and sample standard deviations. We conduct a comprehensive evaluation of TAP, and compare it against two state-of-the-art baselines, namely IntegriDB and Merkle2, showing that the system is practical at scale.
Ulysse Pavloff, Yackolley Amoussou-Guenou, Sara Tucci-Piergiovanni
Ethereum has undergone a recent change called \textit{the Merge}, which made Ethereum a Proof-of-Stake blockchain, shifting closer to BFT consensus. Ethereum, which wished to keep the best of the two protocol designs (BFT and Nakomoto-style), now has a convoluted consensus protocol as its core. The result is a blockchain being possibly produced in a tree-like form while participants try to finalize blocks. We categorize different attacks jeopardizing the liveness of the protocol. The Ethereum community has responded by creating patches against some of them. We discovered a new attack on the patched protocol. To support our analysis, we propose a new high-level formalization of the properties of liveness and availability of the Ethereum blockchain, and we provide a pseudo-code. We believe this formalization to be helpful for other analyses as well. Our results yield that the Ethereum Proof-of-Stake has safety but only probabilistic liveness. The probability of the liveness is influenced by the parameter describing the time frame allowed for validators to change their mind about the current main chain.
In the past few years, the main research efforts regarding General Data Protection Regulation (GDPR)-compliant data sharing have been focused primarily on informed consent (one of the six GDPR lawful bases for data processing). In cases such as Business-to-Business (B2B) and Business-to-Consumer (B2C) data sharing, when consent might not be enough, many small and medium enterprises (SMEs) still depend on contracts—a GDPR basis that is often overlooked due to its complexity. The contract’s lifecycle comprises many stages (e.g., drafting, negotiation, and signing) that must be executed in compliance with GDPR. Despite the active research efforts on digital contracts, contract-based GDPR compliance and challenges such as contract interoperability have not been sufficiently elaborated on yet. Since knowledge graphs and ontologies provide interoperability and support knowledge discovery, we propose and develop a knowledge graph-based tool for GDPR contract compliance verification (CCV). It binds GDPR’s legal basis to data sharing contracts. In addition, we conducted a performance evaluation in terms of execution time and test cases to validate CCV’s correctness in determining the overhead and applicability of the proposed tool in smart city and insurance application scenarios. The evaluation results and the correctness of the CCV tool demonstrate the tool’s practicability for deployment in the real world with minimum overhead.
Threats towards information systems have continued to increase and become more sophisticated, making security approaches a necessity for all types of organizations to ensure their protection. To implement an appropriate computer security policy, it is necessary to efficiently exploit the data that has become a valuable asset for these security systems, provided it is well used, controlled and monitored.In this paper, we focus on developing a decentralized solution based on Blockchain technology and IPFS (InterPlanetary File System) that can maintain and ensure the integrity of log files and sensitive information. The obtained results are promising, we obtained a distributed ledger of all log file transactions in a chronological sequence, which was shared among all Ethereum participants, allowing us to verify the log files' integrity, validity, and auditability throughout their life cycle.
Purpose This paper aims to provide a helpful tool for those who plan to implement blockchain-based solutions for the governance of the electronic medical record (EMR) in health-care settings. The goals are to identify each type of stakeholders involved in these projects and to clarify the relevance, to achieve success, of their readiness, intended as availability and ability to adopt blockchain. Design/methodology/approach The chosen methodology is a multiple case study on three initiatives that used blockchain to manage EMRs. This study relied on multiple sources of evidence. The primary data consisted of two rounds of semi-structured interviews with different informants. This study followed a grounded theory approach and performed within- and cross-case analyses. Findings This study identified the types of stakeholders – nodes and not-nodes – of the network and how their readiness level affects the implementation of blockchain-based projects applied to EMR. The nodes (e.g. patients and doctors) are pivotal in making the network working once this has been constructed. Out of the four readiness dimensions suggested by literature, motivational readiness, has the higher impact. Not-nodes stakeholders play a pivotal role in the project’s pre-implementation phase. For them, structural readiness is the dimension with the higher relevance. Originality/value To the best of the authors’ knowledge, it is the first time that a paper analyses the differences between nodes and not-nodes stakeholders of the blockchain network, in terms not only of type but also of readiness. Identifying the readiness level to implement successful projects is a fundamental step that has never been analysed in the health field.
Sandra Johnson, David Hyland-Wood, Anders L. Madsen, Kerrie Mengersen
The concept of 'Stateless Ethereum' was conceived with the primary aim of mitigating Ethereum's unbounded state growth. The key facilitator of Stateless Ethereum is through the introduction of 'witnesses' into the ecosystem. The changes and potential consequences that these additional data packets pose on the network need to be identified and analysed to ensure that the Ethereum ecosystem can continue operating securely and efficiently. In this paper we propose a Bayesian Network model, a probabilistic graphical modelling approach, to capture the key factors and their interactions in Ethereum mainnet, the public Ethereum blockchain, focussing on the changes being introduced by Stateless Ethereum to estimate the health of the resulting Ethereum ecosystem. We use a mixture of empirical data and expert knowledge, where data are unavailable, to quantify the model. Based on the data and expert knowledge available to use at the time of modelling, the Ethereum ecosystem is expected to remain healthy following the introduction of Stateless Ethereum.
Timotej Knez, Domen Gašperlin, Marko Bajec, Slavko Žitnik
Knowledge graphs are commonly represented by ontology-based databases. Tracking the provenance of ontological changes and ensuring ontology consistency is important. In this work, we propose a transaction manager for ontology-based database manipulation that combines blockchain and Semantic Web technologies. The latter is used for the efficient querying and modification of data, whereas the blockchain is used for the secure storage and tracking of changes. The blockchain enables a decentralized setup and data restoration. We evaluate our solution by measuring cost and time. Our solution introduces some overhead for updates whereas querying works at the same speed as the underlying ontology database.
Zusammenfassung In zunehmend vernetzten Systemen erstreckt sich die gesamte Datenwertschöpfungskette über eine Vielzahl an Systemen, wobei unterschiedliche Akteure mit unterschiedlichen und möglicherweise gegensätzlichen Interessen beteiligt sind. Es ist daher erforderlich, die Prozesse der Datenerfassung, Verarbeitung und Speicherung so abzusichern, dass Manipulationen durch externe Angriffe oder einzelne Akteure erkannt werden können. Dieser Beitrag legt den Fokus auf drei unterschiedliche technische Maßnahmen, durch welche Vertrauen in die ausgetauschten Daten selbst und letztendlich auch zwischen unterschiedlichen Akteuren hergestellt werden kann: Maßnahmen zur Kommunikationssicherheit schützen Daten während des Transports, digitale Kalibrierzertifikate erlauben eine Aussage über die Genauigkeit der erfassten Daten, Distributed-Ledger-Technologien wie zum Beispiel eine Blockchain erfassen Aktionen sowie beteiligte Akteure und legen diese Informationen manipulationsgeschützt ab. Dieser Beitrag bezieht sich auf das Forschungsprojekt GEMIMEG-II , das durch das Bundesministerium für Wirtschaft und Klimaschutz (BMWK) gefördert wird.
Abstract The Gaia-X project was initiated in 2019 by the German and French Ministers of Economy to ensure that companies would not lose control of their industrial data when it is hosted by non-EU cloud service providers. Since then, Gaia-X holds an international association presence in Belgium with more than 334 members, representing both users and providers across 20 countries and 16 national hubs and 5 candidate countries. The Association aims to increase the adoption of cloud services and accelerate data exchanges by European businesses through the facilitation of business data sovereignty with jointly approved (user and provider) policy rules on data portability and interoperability. Although for many enterprises, data sovereignty is seen as a prerequisite for using the cloud, a significant driver to boost the digital economy in business is incentivizing business data sharing. Two decades of cost optimization have constrained business value creation, driving many companies to neglect the opportunity to create shared value within a wider industry ecosystem. Now, thanks to the participation of large numbers of cloud users in the domains of Finance, Health, Energy, Automotive, Travel Aeronautics, Manufacturing, Agriculture, and Mobility, among others, Gaia-X is ideally positioned to help industries define appropriate data spaces and identify/develop compelling use cases, which can then be jointly deployed to a compliant-by-design platform architecture under the Gaia-X specifications, trust, and labeling frameworks. The creation of national Gaia-X hubs that act as independent think tanks, ambassadors, or influencers of the Association further facilitates the emergence of new data spaces and use/enabler cases at a country level, before these are subsequently extended to a European scope and beyond. Gaia-X partners share the view that data spaces will play a similar role in digital business as the web played 40 years ago to help the Internet take off. The Gaia-X Working Groups are at the core of the Gaia-X discussions and deliverables. There are three committees : the Technical, the Policies and Rules, and the Data Spaces and Business. The Technical Committee focus on key architectural elements and their evolution, such as and not limited to: Identity and Access Management: bridge the traditional X509 realm and new SSI realm, creating a decentralized network of identity federations Service Composition: how to assemble services in order to create new services with higher added value Self-Description : how to build digital trust at scale with measurable and comparable criteria The Policy and Rules Committee creates the deliverables required to develop the Gaia-X framework (compliance requirements, labels and qualification processes, credentials matrix, contractual agreements, etc.): The Labels and Qualification working group defines the E2E process for labels and qualification, from defining and evolving the levels of label, the process for defining new labels, and identifying and certifying existing CABS. The Credentials and Trust Anchors working group will develop and maintain a matrix of credentials and their verification methods to enable the implementation of compliance through automation, contractual clauses, certifications, or other methods. The Compliance working group collects compliance requirements from all sources to build a unique compliance requirements pool. The Data Spaces Business Committee helps the Association expanding and accelerating the creation of new Gaia-X service in the market: The Finance working group focuses on business modeling and supports the project office of the Association. The Technical working group analyzes the technical requirements from a business perspective. The Operational Requirements working group is the business requirements unit. The Hub working groups hold close contact with all Gaia-X Hubs and support the collection and creation of the Gaia-X use and business cases. These working groups maintain the international list of all use cases and data spaces and coordinate the Hubs.
Abstract With an increasing affinity towards patient‐centric care, sharing real‐time sensitive data for collaboration between multiple parties with finer access control becomes critical. Most existing studies based on the blockchain technology in the medical field discuss various application scenarios and security aspects, without focusing on data ownership, secure data sharing, or finer access control. In this work, a non‐fungible token (NFT)‐based system is proposed to implement a health record marketplace. The system leverages the NFT technology to provide dual ownership along with finer access control and efficiency in data sharing. The advantage of permissioned blockchain along with InterPlanetary File System (IPFS) are taken for off‐chain data storage to improve security and efficiency. Because price determination is critical in the market, Stackelberg game theory is utilized to determine pricing strategies for both data owners and consumers. Also, to efficiently achieve finer access control, a popularity‐based adaptive NFT management scheme using reinforcement learning is proposed. Simulation experiments are carried out to demonstrate accuracy and efficiency of our proposed schemes.
Miguel Morales‐Sandoval, J. A. Llorens Molina, Heidy M. Marin-Castro, J. L. Gonzalez‐Compean
In an Inter-Organizational Business Process (IOBP), independent organizations (collaborators) exchange messages to perform business transactions. With process mining, the collaborators could know what they are actually doing from process execution data and take actions for improving the underlying business process. However, process mining assumes that the knowledge of the entire process is available, something that is difficult to achieve in IOBPs since process execution data generally is not shared among the collaborating entities due to regulations and confidentiality policies (exposure of customers’ data or business secrets). Additionally, there is an inherently lack-of-trust problem in IOBP as the collaborators are mutually untrusted and executed IOBP can be subject to dispute on counterfeiting actions. Recently, Blockchain has been suggested for IOBP execution management to mitigate the lack-of-trust problem. Independently, some works have suggested the use of Blockchain to support process mining tasks. In this paper, we study and address the problem of IOBP mining whose management and execution is supported by Blockchain. As contribution, we present an approach that takes advantage of Blockchain capabilities to tackle, at the same time, the lack-of-trust problem (management and execution) and confident execution data collection for process mining (discovery and conformance) of IOBPs. We present a method that (i) ensures the business rules for the correct execution and monitoring of the IOBP by collaborators, (ii) creates the event log, with data cleaning integrated, at the time the IOBP executes, and (iii) produces useful event log in XES and CSV format for the discovery and conformance checking tasks in process mining. By a set of experiments on real IOBPs, we validate our method and evaluate its impact in the resulting discovered models (fitness and precision metrics). Results revealed the effectiveness of our method to cope with both the lack-of-trust problem in IOBPs at the time that contributes to collect the data for process mining. Our method was implemented as a software tool available to the community as open-source code.
Youcef Djenouri, Gautam Srivastava, Asma Belhadi, Jerry Chun‐Wei Lin
Abstract This article introduces a new problem of distributed knowledge graph, in IoT 5G setting. We developed an end‐to‐end solution for solving such problem by exploring the blockchain management and intelligent method for producing the better matching of the concepts and relations of the set of knowledge graphs. The concepts and the relations of the knowledge graphs are divided into several components, each of which contains similar concepts and relations. Instead of exploring the whole concepts and the relations of the knowledge graphs, only the representative of these components is compared during the matching process. The framework has outperformed state‐of‐the‐art knowledge graph matching algorithms using different scenarios as input in the experiments. In addition, to confirm the usability of our suggested framework, an in‐depth experimental analysis has been done; the results are very promising in both runtime and accuracy.
The global supply chain is a network of interconnected processes that create, use, and exchange records, but which were not designed to interact with one another. As such, the key to unlocking the full potential of supply chain management (SCM) technologies is achieving interoperability across participating records systems and networks. We review existing research and solutions using distributed ledger technology (DLT) and provide a survey of its current state of practice. We additionally propose a holistic solution: a DLT-based interoperable future state that could enable the interoperable, efficient, reliable, and secure exchange of records with integrity. Finally, we provide a gap analysis between our proposed future state and the current state, which also serves as a gap analysis for many fractional DLT-based SCM solutions and research.
Sidra Malik, Volkan Dedeoglu, Salil S. Kanhere, Raja Jurdak
Blockchain offers traceability and transparency to supply chain event data and hence can help overcome many challenges in supply chain management such as: data integrity, provenance and traceability. However, data privacy concerns such as the protection of trade secrets have hindered adoption of blockchain technology. Although consortium blockchains only allow authorised supply chain entities to read/write to the ledger, privacy preservation of trade secrets cannot be ascertained. In this work, we propose a privacy-preservation framework, PrivChain, to protect sensitive data on blockchain using zero knowledge proofs. PrivChain provides provenance and traceability without revealing any sensitive information to end-consumers or supply chain entities. Its novelty stems from: a) its ability to allow data owners to protect trade related information and instead provide proofs on the data, and b) an integrated incentive mechanism for entities providing valid proofs over provenance data. In particular, PrivChain uses Zero Knowledge Range Proofs (ZKRPs), an efficient variant of ZKPs, to provide origin information without disclosing the exact location of a supply chain product. Furthermore, the framework allows to compute proofs and commitments off-line, decoupling the computational overhead from blockchain. The proof verification process and incentive payment initiation are automated using blockchain transactions, smart contracts, and events. A proof of concept implementation on Hyperledger Fabric reveals a minimal overhead of using PrivChain for blockchain enabled supply chains.
DNS has always been criticized for its inherent design flaws, making the system vulnerable to kinds of attacks. Besides, DNS domain names are not fully controlled by the users, which can be easily taken down by the authorities and registrars. Since blockchain has its unique properties like immutability and decentralization, it seems to be promising to build a decentralized name service on blockchain. Ethereum Name Service (ENS), as a novel name service built atop Etheruem, has received great attention from the community. Yet, no existing work has systematically studied this emerging system, especially the security issues and misbehaviors in ENS. To fill the void, we present the first large-scale study of ENS by collecting and analyzing millions of event logs related to ENS. We characterize the ENS system from a number of perspectives. Our findings suggest that ENS is showing gradually popularity during its four years' evolution, mainly due to its distributed and open nature that ENS domain names can be set to any kinds of records, even censored and malicious contents. We have identified several security issues and misbehaviors including traditional DNS security issues and new issues introduced by ENS smart contracts. Attackers are abusing the system with thousands of squatting ENS names, a number of scam blockchain addresses and malicious websites, etc. Our exploration suggests that our community should invest more effort into the detection and mitigation of issues in Blockchain-based Name Services towards building an open and trustworthy name service.
Hao Sen Andrew Fang, Teng Hwee Tan, Cheryl Yan Fang Tan, Marcus Chun Jin Tan
BACKGROUND: Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing. OBJECTIVE: This review aims to examine the current landscape, design choices, limitations, and future directions of blockchain-based PHRs. METHODS: Adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, a cross-disciplinary systematic review was performed in July 2020 on all eligible articles, including gray literature, from the following 8 databases: ACM, IEEE Xplore, MEDLINE, ScienceDirect, Scopus, SpringerLink, Web of Science, and Google Scholar. Three reviewers independently performed a full-text review and data abstraction using a standardized data collection form. RESULTS: A total of 58 articles met the inclusion criteria. In the review, we found that the blockchain PHR space has matured over the past 5 years, from purely conceptual ideas initially to an increasing trend of publications describing prototypes and even implementations. Although the eventual application of blockchain in PHRs is intended for the health care industry, the majority of the articles were found in engineering or computer science publications. Among the blockchain PHRs described, permissioned blockchains and off-chain storage were the most common design choices. Although 18 articles described a tethered blockchain PHR, all of them were at the conceptual stage. CONCLUSIONS: This review revealed that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage. Being the first systematic review on blockchain PHRs, this review should serve as a basis for future reviews to track the development of the space.
Jieh‐Haur Chen, Mu‐Chun Su, Vidya Trisandini Azzizi, Ting-Kwei Wang · 5 authors
Technological developments have made the construction industry efficient. The aim of this research is to solve communication interaction problems to build a project management platform using the interactive concept of natural language processing technology. A comprehensive literature review and expert interviews associated with techniques dealing with natural languages suggests the proposed system containing the Progressive Scale Expansion Network (PSENet), Convolutional Recurrent Neural Network (CRNN), and Bi-directional Recurrent Neutral Networks Convolutional Recurrent Neural Network (BRNN-CNN) toolboxes to extract the key words for construction projects contracts. The results show that a fully automatic platform facilitating contract management is achieved. For academic domains, the Contract Keyword Detection (CKD) mechanism integrating PSENet, CRNN, and BRNN-CNN approaches to cope with real-time massive document flows is novel in the construction industry. For practice, the proposed approach brings significant reduction for manpower and human error, an alternative for settling down misunderstanding or disputes due to real-time and precise communication, and a solution for efficient documentary management. It connects all contract stakeholders proficiently.
Jan 1, 2021·Proceedings of the 36th IFIP TC 11 International Conference on ICT Systems Security and Privacy Protection (IFIP SEC 2021)/IFIP Advances in Information and Communication Technology, vol. 625, 2021, 19-35
Stefan More, Peter Grassberger, Felix Hörandner, Andreas Abraham · 5 authors
Recruiters and institutions around the world struggle with the verification of diplomas issued in a diverse and global education setting. Firstly, it is a nontrivial problem to identify bogus institutions selling education credentials. While institutions are often accredited by qualified authorities on a regional level, there is no global authority fulfilling this task. Secondly, many different data schemas are used to encode education credentials, which represents a considerable challenge to automated processing. Consequently, significant manual effort is required to verify credentials. In this paper, we tackle these challenges by introducing a decentralized and open system to automatically verify the legitimacy of issuers and interpret credentials in unknown schemas. We do so by enabling participants to publish transformation information, which enables verifiers to transform credentials into their preferred schema. Due to the lack of a global root of trust, we utilize a distributed ledger to build a decentralized web of trust, which verifiers can query to gather information on the trustworthiness of issuing institutions and to establish trust in transformation information. Going beyond diploma fraud, our system can be generalized to tackle the generalized problem for other domains lacking a root of trust and agreements on data schemas.
The temporal nature of modeling accounts as nodes and transactions as\ndirected edges in a directed graph -- for a blockchain, enables us to\nunderstand the behavior (malicious or benign) of the accounts. Predictive\nclassification of accounts as malicious or benign could help users of the\npermissionless blockchain platforms to operate in a secure manner. Motivated by\nthis, we introduce temporal features such as burst and attractiveness on top of\nseveral already used graph properties such as the node degree and clustering\ncoefficient. Using identified features, we train various Machine Learning (ML)\nalgorithms and identify the algorithm that performs the best in detecting which\naccounts are malicious. We then study the behavior of the accounts over\ndifferent temporal granularities of the dataset before assigning them malicious\ntags. For Ethereum blockchain, we identify that for the entire dataset - the\nExtraTreesClassifier performs the best among supervised ML algorithms. On the\nother hand, using cosine similarity on top of the results provided by\nunsupervised ML algorithms such as K-Means on the entire dataset, we were able\nto detect 554 more suspicious accounts. Further, using behavior change analysis\nfor accounts, we identify 814 unique suspicious accounts across different\ntemporal granularities.\n
Gamze Gürsoy, Robert Bjornson, Molly E. Green, Mark Gerstein
BACKGROUND: Genomic variants are considered sensitive information, revealing potentially private facts about individuals. Therefore, it is important to control access to such data. A key aspect of controlled access is secure storage and efficient query of access logs, for potential misuse. However, there are challenges to securing logs, such as designing against the consequences of "single points of failure". A potential approach to circumvent these challenges is blockchain technology, which is currently popular in cryptocurrency due to its properties of security, immutability, and decentralization. One of the tasks of the iDASH (Integrating Data for Analysis, Anonymization, and Sharing) Secure Genome Analysis Competition in 2018 was to develop time- and space-efficient blockchain-based ledgering solutions to log and query user activity accessing genomic datasets across multiple sites, using MultiChain. METHODS: MultiChain is a specific blockchain platform that offers "data streams" embedded in the chain for rapid and secure data storage. We devised a storage protocol taking advantage of the keys in the MultiChain data streams and created a data frame from the chain allowing efficient query. Our solution to the iDASH competition was selected as the winner at a workshop held in San Diego, CA in October 2018. Although our solution worked well in the challenge, it has the drawback that it requires downloading all the data from the chain and keeping it locally in memory for fast query. To address this, we provide an alternate "bigmem" solution that uses indices rather than local storage for rapid queries. RESULTS: We profiled the performance of both of our solutions using logs with 100,000 to 600,000 entries, both for querying the chain and inserting data into it. The challenge solution requires 12 seconds time and 120 Mb of memory for querying from 100,000 entries. The memory requirement increases linearly and reaches 470 MB for a chain with 600,000 entries. Although our alternate bigmem solution is slower and requires more memory (408 seconds and 250 MB, respectively, for 100,000 entries), the memory requirement increases at a slower rate and reaches only 360 MB for 600,000 entries. CONCLUSION: Overall, we demonstrate that genomic access log files can be stored and queried efficiently with blockchain. Beyond this, our protocol potentially could be applied to other types of health data such as electronic health records.
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.
Maher Alharby, Roben Castagna Lunardi, Amjad Aldweesh, Aad van Moorsel
In proof-of-work based blockchains such as Ethereum, verification of blocks is an integral part of establishing consensus across nodes. However, in Ethereum, miners do not receive a reward for verifying. This implies that miners face the Verifier's Dilemma: use resources for verification, or use them for the more lucrative mining of new blocks? We provide an extensive analysis of the Verifier's Dilemma, using a data-driven model-based approach that combines closed-form expressions, machine learning techniques and discrete-event simulation. We collect data from over 300,000 smart contracts and experimentally obtain their CPU execution times. Gaussian Mixture Models and Random Forest Regression transform the data into distributions and inputs suitable for the simulator. We show that, indeed, it is often economically rational not to verify, in particular for miners with less hashing power. We consider two approaches to mitigate the implications of the Verifier's Dilemma, namely parallelization and active insertion of invalid blocks, both will be shown to be effective.