Prototype of running clinical trials in an untrustworthy environment using blockchain.

Monitoring and ensuring the integrity of data within the clinical trial process is currently not always feasible with the current research system. We propose a blockchain-based system to make data collected in the clinical trial process immutable, traceable, and potentially more trustworthy. We use raw data from a real completed clinical trial, simulate the trial onto a proof of concept web portal service, and test its resilience to data tampering. We also assess its prospects to provide a traceable and useful audit trail of trial data for regulators, and a flexible service for all members within the clinical trials network. We also improve the way adverse events are currently reported. In conclusion, we advocate that this service could offer an improvement in clinical trial data management, and could bolster trust in the clinical research process and the ease at which regulators can oversee trials

Privacy-preserving model learning on a blockchain network-of-networks.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction

Blockchain, Leadership And Management: Business AS Usual Or Radical Disruption?

The Internet provided the world with interconnection. However, it did not provide it with trust. Trust is lacking everywhere in our society and is the reason for the existence of powerful intermediaries aggregating power. Trust is what prevents the digital world to take over. This has consequences for organisations: they are inefficient because time, energy, money and passion are wasted on verifying everything happens as decided. Managers play the role of intermediaries in such case: they connect experts with each others and instruct them of what to do. As a result, in our expert society, people's engagement is low because no one is there to inspire and empower them. In other words, our society faces an unprecedented lack of leadership. Provided all those shortcomings, the study imagines the potential repercussions, especially in the context of management, of implementing a blockchain infrastructure in any type of organisation. Indeed, the blockchain technology seems to be able to remedy to those issues, for this distributed and immutable ledger provides security, decentralisation and transparency. In the context of a blockchain economy, the findings show that value creation will be rearranged, with experts directly collaborating with each others, and hierarchy being eliminated. This could, in turn, render managers obsolete, as a blockchain infrastructure will automate most of the tasks. As a result, only a strong, action-oriented, leadership would maintain the organisation together. This leadership-in-action would consist in igniting people to take action; coach members of the organisations so that their contribution makes sense in the greater context of life

Link Before You Share: Managing Privacy Policies through Blockchain

With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. Users consent to providers to gather and share their Personally Identifiable Information (PII). We have developed a novel framework to automatically track details about how a users' PII data is stored, used and shared by the provider. We have integrated our Data Privacy ontology with the properties of blockchain, to develop an automated access control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by Big Data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.Comment: 10 pages, 6 figures, Published in: 4th International Workshop on Privacy and Security of Big Data (PSBD 2017) in conjunction with 2017 IEEE International Conference on Big Data (IEEE BigData 2017) December 14, 2017, Boston, MA, US

Using Blockchain Technology for The Organ Procurement and Transplant Network

The organ donation system in the United States is centralized and difficult to audit by the general public. This centralized approach may lead to data integrity issues in the future. The Organ Procurement and Transplant Network (OPTN) was built and maintained by a non-governmental organization called the United Network for Organ Sharing (UNOS) under its proprietary UNet(SM) umbrella platform. This platform is made up of proprietary closed source software and does not provide the general public easy access to the organ transplant data for auditing. This study investigates the feasibility, challenges, and advantages of a blockchain-based OPTN. A prototype of a blockchain-based OPTN was created using the Hyperledger Fabric framework. The policies and guidelines issued by the United States Department of Health and Human Services for UNOS and the OPTN were used as the basis of this prototype. Four factors were identified to have a direct effect on the performance of this system, viz. max batch time out, max block size, endorsement policy, and transaction rate. Additionally, two variants of the blockchain chaincode were also developed. The first variant performed the organ-candidate matching inside the blockchain (Scheme A), and the second variant performed it outside the blockchain (Scheme B). Analysis of these data showed that Scheme A outperformed Scheme B in all experiments for write-operations. However, the read operations remained unaffected by any of the experiment variables in the given environment. Based on these results, it is recommended to perform the organ-candidate matching on the blockchain with the max batch time out close to the transaction rate