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.
Roberta Galici, Laura Ordile, Michele Marchesi, Andrea Pinna · 5 authors
We present a novel strategy, based on the Extract, Transform and Load (ETL) process, to collect data from a blockchain, elaborate and make it available for further analysis. The study aims to satisfy the need for increasingly efficient data extraction strategies and effective representation methods for blockchain data. For this reason, we conceived a system to make scalable the process of blockchain data extraction and clustering, and to provide a SQL database which preserves the distinction between transaction and addresses. The proposed system satisfies the need to cluster addresses in entities, and the need to store the extracted data in a conventional database, making possible the data analysis by querying the database. In general, ETL processes allow the automation of the operation of data selection, data collection and data conditioning from a data warehouse, and produce output data in the best format for subsequent processing or for business. We focus on the Bitcoin blockchain transactions, which we organized in a relational database to distinguish between the input section and the output section of each transaction. We describe the implementation of address clustering algorithms specific for the Bitcoin blockchain and the process to collect and transform data and to load them in the database. To balance the input data rate with the elaboration time, we manage blockchain data according to the lambda architecture. To evaluate our process, we first analyzed the performances in terms of scalability, and then we checked its usability by analyzing loaded data. Finally, we present the results of a toy analysis, which provides some findings about blockchain data, focusing on a comparison between the statistics of the last year of transactions, and previous results of historical blockchain data found in the literature. The ETL process we realized to analyze blockchain data is proven to be able to perform a reliable and scalable data acquisition process, whose result makes stored data available for further analysis and business.
Daniel Burkhardt, Nana Agyei-Kena, Patrick Frey, Sven Kurrle · 5 authors
Data silos in many system landscapes complicate the creation of comprehensive information. Distributed Ledger Technology enables trust between assets via a distributed, secure and immutable storage of transactions. Deep Learning realizes intelligence to make decisions, conduct them and analyze their results based on the gathered data. In order to counteract the limitations of current system landscapes, an integrative implication of both Distributed Ledger Technology and Deep Learning is needed. Many considerations arise during the design of information systems integrating the named technologies. Transparency over the training and deployment of various Deep Learning methods on a distributed data landscape needs to be achieved. Using both a literature review and a qualitative research approach, this paper describes the development of design patterns and their selection criteria with different dimensions taken into consideration. The evaluation phase comprises of semi-structured interviews with experts from different disciplines. The result of this paper guides stakeholders in the selection of a suitable technical solution.
Initiatives leveraging the emerging SMART on FHIR standard are promising healthcare system improvements while reducing information technology costs with reusable system components. Previously closed Electronic Medical Record systems are gradually opened to third party applications through FHIR-enabled APIs. While this allows for technical interoperability, patient safety concerns from data quality issues with the underlying system data remain unsolved. We propose to apply a “Data Quality by Contract” approach to pre- and post-conditions of data use cases to assure safe operation of SMART on FHIR apps. We demonstrate how a cardiac risk scoring app could leverage data quality probes to validate several data quality concerns.
Businesses whose work hours, work durations & also more compliance records must be trustworthy and verifiable have great difficulties maintaining more accurate & also safe timekeeping. This article looks at a novel approach for smart contract & also blockchain technology integration protection of timekeeping data. Changing from more conventional, centralized time-tracking systems to a distributed ledger creates a safe framework that logs time inputs as unchangeable transactions, therefore preventing any retroactive changes or more unauthorized additions. While smart contracts provide automation of activities like clock-ins, clock-outs & more compliance alerts, every input is validated & also documented with cryptographic certainty. This guarantees real-time policy compliance and eliminates errors in hand-made monitoring. Clear audit trail, real-time notifications for errors or transgressions, and tamper-evident recordkeeping define the main characteristics of the recommended solution. These elements taken together provide a mechanism wherein auditors, managers, and employees may trust the data free from middlemen's intervention. Extended data integrity, increased organizational trust, greater transparency in payroll and compliance procedures, and fewer incidence of fraud or human error define the projected benefits. From manufacturing to logistics, remote work, healthcare, legal compliance management, the approach is versatile and scalable across numerous sectors. This work integrates trust into the infrastructure via blockchain and smart contract automation therefore establishing a progressive base for future timekeeping systems
We present a novel tree-based technique that can convert any designated-prover NIZK proof system (DP-NIZK) which maintains zero-knowledge only for single statement, into one that allows to prove an unlimited number of statements in ZK, while maintaining all parameters succinct. Our transformation requires leveled fully-homomorphic encryption. We note that single-statement DP-NIZK can be constructed from any one-way function. We also observe a two-way derivation between DP-NIZK and attribute-based signatures (ABS), and as a result derive now constructions of ABS and homomorphic signatures (HS).
BACKGROUND: Health care professionals are required to maintain accurate health records of patients. Furthermore, these records should be shared across different health care organizations for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to the implementation of these goals and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as concerns about performance and privacy issues. OBJECTIVE: This study aimed to build and evaluate an experimental blockchain for EHRs, named HealthChain, which overcomes the disadvantages of traditional EHR systems. METHODS: HealthChain is built based on consortium blockchain technology. Specifically, three organizations, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Every peer node hosts an instance of the distributed ledger consisting of EHRs and an instance of chaincode regulating the permissions of participants. Designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers. RESULTS: HealthChain achieves functional and nonfunctional requirements. It can store EHRs in a distributed ledger and share them among different participants. Moreover, it demonstrates superior features, such as privacy preservation, security, and high throughput. These are the main reasons why HealthChain is proposed. CONCLUSIONS: Consortium blockchain technology can help to build new EHR systems and solve the problems that prevent the adoption of traditional systems.
Data mesh architecture is revolutionizing the field of data analytics, offering a fresh approach to how organizations manage and utilize data. Traditionally, centralized data warehouses and data lakes have dominated the landscape, but as the volume and complexity of data grow, these systems struggle to keep pace. Data mesh breaks away from the centralized model by promoting a decentralized and domain-oriented approach, where individual teams take ownership of their data. This shift enables organizations to treat data as a product, with each domain team responsible for managing and serving their data assets. The goal is to make data more accessible and usable across the organization while reducing bottlenecks that occur in centralized systems. Implementing data mesh architecture has profound implications for data management. It encourages better data governance, scalability, and flexibility, while fostering innovation and collaboration. However, it also introduces challenges, such as ensuring consistency, standardization, and proper integration across domains. The success of a data mesh strategy depends on a strong cultural shift, requiring both technical infrastructure and organizational alignment to support autonomous teams. Ultimately, data mesh offers the promise of unlocking the full potential of data in large organizations by making it more agile, democratized, and efficient. By embracing this architecture, businesses can better respond to the increasing demands of modern data management and gain a competitive edge in the digital landscape.
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
Process Mining (PM) is a special data mining technique that allows extracting information from data of critical transactions (i.e. event logs) carried out in Information Systems and monitors the patterns in these transactions. When we start to process event logs with process mining tools, we face with data quality problems such as incorrect and insufficient logging and timing. Thus, data cleaning operations must be applied to event logs before applying process mining on these logs. Being an innovative medium of distributed data processing and storage with the features of enhanced security, traceability, automated transaction verification and integration, Blockchain Technology and Smart Contracts might be a good option to process and store event logs for process mining. In this paper, we focused on the cleaning of the event logs by smart contract as data is flowing from the information systems into the blockchain, and used Hyperledger Composer by IBM to develop our solution. We tested our proposal on an open process data of 1555 records, and compared the cleaning performance of our proposal with that of DataWrangler by Stanford University. Our proposal not only cleaned all 1313 records identified and cleaned by DataWrangler, it also saved 12 additional records with a different date format that was caught and corrected by our smart contract implementation.
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.
Visualization is a widely used in different fields of studies such as supply chain management when there is a need to communicate information to general users. However, there are multiple limitations and problems with visualizing information within traditional systems. In traditional systems, data is in control of one single authority; so data is mutable and there is no guarantee that system administer does not change the data to achieve a desired result. Besides, such systems are not transparent and users do not have any access to the data flow. In this thesis, the main goal was to visualize information that has been saved on top of a new technology named blockchain to overcome the aforementioned problems. All the records in the system are saved on the blockchain and data is pulled out from blockchain to be used in visualization. To have a better insight, a review has been done on relevant studies about blockchain, supply chain and visualization. After identifying the gap in literature review, an architecture was proposed that was used in the implementation. The implementation contains, a system on top of ethereum blockchain and front-end which allows users to interact with the system. In the system, all the information about products and all the transactions that ever happened in the system, are recorded on the blockchain. Then, data was retrieved from the blockchain and used to visualize provenance of products on Google Map API. After implementing the system, the performance was evaluated to make sure that it can handle different situations where various number of clients sending request to the system simultaneously. The performance was as expected in which system responds longer when number of clients sending requests were growing. \nThe proposed solution fill the gap that was identified in the literature review. By adding provenance visualization users can explore previous owners and locations of a product in a trustable manner. Future research can focus on analysis of data which will allow organizations to make informed decisions on choosing popular products to sell.
With data intensive computing helping advance state-of-the-art in varied fields, data provenance and lineage continue to remain formidable challenges in assisting with integrity and reproducibility in research and applications. This is particularly challenging for distributed scenarios, where data may be originating from decentralized sources without any centralized control by a single trusted entity. To date most of the data provenance systems are specific to particular domains, and are often centralized. Distributed ledgers such as blockchains have proved quite popular and effective in addressing trust and consensus without central control. There are a few recent proposals to employ blockchains for data provenance, however, they rely on currency in order to propose transactions using public blockchains.\n\nWe present HyperProv, a general framework for data provenance based on the permissioned blockchain Hyperledger Fabric (HLF), and to the best of our knowledge, the first provenance system that is ported to ARM based devices such as Raspberry Pi (RPi). HyperProv records the operation history and data lineage by tracking checksums, editors, timestamps, data pointers, dependencies, and more. Provenance data is retrieved and stored through a NodeJS client library to simplify interactions with the blockchain. HyperProv has a set of built-in queries using smart contracts that enable lightweight retrieval of large collections of provenance data. We evaluate the throughput, latency and resource consumption of HyperProv on x86-64 desktop machines, as well as RPi, demonstrating the feasibility of using HyperProv on RPi for tamperproof data provenance, useful in particular for Internet of Things use cases.
Apr 20, 2019·MaxEnt 2019 - Proceedings of the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Garching, Germany, 30 June - 5 July 2019
Randomization is an integral part of well-designed statistical trials, and is also a required procedure in legal systems. Implementation of honest, unbiased, understandable, secure, traceable, auditable and collusion resistant randomization procedures is a mater of great legal, social and political importance. Given the juridical and social importance of randomization, it is important to develop procedures in full compliance with the following desiderata: (a) Statistical soundness and computational efficiency; (b) Procedural, cryptographical and computational security; (c) Complete auditability and traceability; (d) Any attempt by participating parties or coalitions to spuriously influence the procedure should be either unsuccessful or be detected; (e) Open-source programming; (f) Multiple hardware platform and operating system implementation; (g) User friendliness and transparency; (h) Flexibility and adaptability for the needs and requirements of multiple application areas (like, for example, clinical trials, selection of jury or judges in legal proceedings, and draft lotteries). This paper presents a simple and easy to implement randomization protocol that assures, in a formal mathematical setting, full compliance to the aforementioned desiderata for randomization procedures.
Sebastian Lawrenz, Priyanka Sharma, Andreas Rausch
In the digital Economy 'Data is the new oil. In the last decade technology has disrupted every filed imaginable. One such booming technology is Blockchain. A blockchain is essentially a distributed database of records or public ledger of all transactions or digital events that have been executed and shared among participating parties. And once entered, the information is immutable. Ongoing projects and prior work in the fields of big data, data mining and data science has revealed how relevant data can be used to enhance products and services. There are uncountable applications and advantages of relevant data. The most valuable companies of today treat data as a commodity, which they trade and earn revenues.
David Froelicher, Juan Ramón Troncoso-Pastoriza, João M. C. Sousa, Jean‐Pierre Hubaux
Data sharing has become of primary importance in many domains such as\nbig-data analytics, economics and medical research, but remains difficult to\nachieve when the data are sensitive. In fact, sharing personal information\nrequires individuals' unconditional consent or is often simply forbidden for\nprivacy and security reasons. In this paper, we propose Drynx, a decentralized\nsystem for privacy-conscious statistical analysis on distributed datasets.\nDrynx relies on a set of computing nodes to enable the computation of\nstatistics such as standard deviation or extrema, and the training and\nevaluation of machine-learning models on sensitive and distributed data. To\nensure data confidentiality and the privacy of the data providers, Drynx\ncombines interactive protocols, homomorphic encryption, zero-knowledge proofs\nof correctness, and differential privacy. It enables an efficient and\ndecentralized verification of the input data and of all the system's\ncomputations thus provides auditability in a strong adversarial model in which\nno entity has to be individually trusted. Drynx is highly modular, dynamic and\nparallelizable. Our evaluation shows that it enables the training of a logistic\nregression model on a dataset (12 features and 600,000 records) distributed\namong 12 data providers in less than 2 seconds. The computations are\ndistributed among 6 computing nodes, and Drynx enables the verification of the\nquery execution's correctness in less than 22 seconds.\n