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The potential of Big Data allows us to hope to slow the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine. Yet, as with the Internet, social media, and cloud computing, early enthusiasts are creating hyperbolic expectations about how and how quickly Big Data will transform healthcare. A number of issues challenge the adoption and success of healthcare Big Data, including privacy and security, who owns the data, and the regulatory labyrinth. Furthermore, real advances depend on better ways to exploit the disconnected puddles and lakes of existing data as well as better ways to generate, capture, analyze and make use of the streams of new kinds of data that are about to flood healthcare. This paper will introduce to Big Data and explore how it is becoming a growing force in the changing healthcare landscape.

Big Data in Healthcare Hype and Hope

Conference proceedings on Object-oriented programming systems, languages, and applications

With the rapid development of healthcare-based cyber physical systems (CPSs), more and more healthcare data are collected from clinical institutions or hospitals. Due to the private and fragmented nature, healthcare data is quite suitable to be processed by federated learning (FL) paradigm, where a shared global model is aggregated by a central server while keeping the sensitive healthcare data in local hospitals. However, there are two practical issues: (1) the centralized FL server may not honestly aggregate the final model, and (2) the FL participants lack incentive to contribute their efforts. In this study, we propose a blockchain-empowered FL framework for healthcare-based CPSs. A distributed ledger is maintained by a task agreement committee which is composed by the representators of the hospitals who execute FL tasks. A secure FL task model training-based consensus process is proposed to generate consistent blocks. Furthermore, an contribution point-based incentive mechanism is designed to fairly reward FL participators for contributing their local data. We evaluate the proposed system base on real healthcare data and the numerical results demonstrate its effectiveness in achieving FL model aggregation truthfulness and efficiency in providing incentives for FL participants. 

A Blockchain-empowered Federated Learning in Healthcare-based Cyber Physical Systems

Emission trading policy provides a new approach using economic incentives to control the environmental pollution efficiently. Legal polluters can trade emission permits with each other through a trusted trading system that lacks security and credibility due to its centralization nowadays. Permissioned blockchain utilize a decentralized way to store private data immutably, providing new approaches to solve those defects of the existing centralized systems. In this paper, we propose a Hyperledger-based Emission Trading System (HyperETS) on the permissioned blockchain. Using Hyperledger Fabric as the implementation platform, HyperETS integrates the fine-grained access control, distributed ledger, and consensus protocol, aiming to provide credible trading service for polluters. We achieve the business logic by designing the particular ledger structures and smart contract in blockchain. HyperETS stores all transactions immutably in a chain and makes it easy to share the data between organizations. Finally, several experiments are conducted to evaluate the performances of the proposed demonstration system.

/pdf/design-and-implementation-on-hyperledger-based-emission-4li10gn56i.pdf

Design and Implementation on Hyperledger-Based Emission Trading System

The advent of healthcare information management systems &#x0028 HIMSs &#x0029 continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics &#x0028 BDA &#x0029 in healthcare can, for instance, help determine causes of diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments, generate accurate predictions of readmissions, enhance clinical care, and pinpoint opportunities for cost savings. However, BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners. In this paper, we present a comprehensive roadmap to derive insights from BDA in the healthcare &#x0028 patient care &#x0029 domain, based on the results of a systematic literature review. We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on NoSQL databases. We also identify the limitations and challenges of these applications and justify the potential of NoSQL databases to address these challenges and further enhance BDA healthcare research. We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm. We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare. Finally, we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work. The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators, practitioners and professionals to successfully implement BDA initiatives in their organizations.

Big data analytics in healthcare &#x2212 A systematic literature review and roadmap for practical implementation

Security models, developed for databases, differ in many aspects because they focus on different features of the database security problem or because they make different assumptions about what constitutes a secure database This leads to disjointed and incomplete understanding of the organizational security strategy This makes it difficult to reconcile different security requirements This paper discusses the various security issues in databases This approach is useful for the planning of explicit and directive based database security requirements

Security Issues in Databases

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Big data analytics in healthcare &#x2212 A systematic literature review and roadmap for practical implementation

Security Issues in Databases