4.6 Article

Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control

Journal

JOURNAL OF MEDICAL SYSTEMS
Volume 40, Issue 10, Pages -

Publisher

SPRINGER
DOI: 10.1007/s10916-016-0574-6

Keywords

Healthcare data system; Indicator-centric schema; BlockChain; Healthcare data sharing; Privacy risk

Funding

  1. Humanity and Social Science Youth Foundation of Ministry of Education of China [14YJC630181]
  2. Fundamental Research Funds for the Central Universities of China [JB-SK1206]
  3. Scientific Research Fund for Talent Introduction of Zhongnan University of Economics and Law [21141611313]
  4. Scientific Research Fund for Talent Introduction of Huaqiao University [12Y0324]
  5. National Social Science Foundation for Young Scholars [13CTJ003]

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Healthcare data are a valuable source of healthcare intelligence. Sharing of healthcare data is one essential step to make healthcare system smarter and improve the quality of healthcare service. Healthcare data, one personal asset of patient, should be owned and controlled by patient, instead of being scattered in different healthcare systems, which prevents data sharing and puts patient privacy at risks. Blockchain is demonstrated in the financial field that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we proposed an App (called Healthcare Data Gateway (HGD)) architecture based on blockchain to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private. Our proposed purpose-centric access model ensures patient own and control their healthcare data; simple unified Indicator-Centric Schema (ICS) makes it possible to organize all kinds of personal healthcare data practically and easily. We also point out that MPC (Secure Multi-Party Computing) is one promising solution to enable untrusted third-party to conduct computation over patient data without violating privacy.

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