John K.W. Yeung, Simon C.W. Wong, Gabriel H.H. Chan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)


Inspired by the challenges of keeping secure and private lifelong longitudinal health-related transactions in a distributed and shared, encrypted and access controlled, immutable and rapidly growing blockchain for public health, a cloud infrastructure with machine learning (ML), data analytics, security and privacy considerations has to be designed. This chapter presents how this proposed cloud infrastructure facilitates distribution and sharing of health transactions and discusses how the security and privacy mechanisms work for the public health stakeholders to access and analyse anonymous health data stored in the blockchain in the cloud platform. Also, the immutability and rapid volume growth features of the historical transactional data stored on the public health blockchain provide big data for the health care practitioners, researchers and government to analyse and get insights from. In this regard, those transactional data relationships have to be managed effectively for efficient data processing and data analytics integrated with learning machine models can be adopted to examine the data for sophisticated analyses for health care concerns. This chapter exhibits how ML can be integrated into data analytics and highlights some applications on cloud using Amazon Web Services as an example.

Original languageEnglish
Title of host publicationThe Routledge Handbook of Public Health and the Community
PublisherTaylor and Francis
Number of pages10
ISBN (Electronic)9781000427363
ISBN (Print)9780367634193
Publication statusPublished - 1 Jan 2021


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