Previously submitted to: JMIR Medical Informatics (no longer under consideration since Nov 06, 2020)
Date Submitted: Jan 15, 2018
Open Peer Review Period: Jan 19, 2018 - Mar 29, 2018
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A Survey of Privacy-Preserving Techniques for Reuse of Distributed Health Data
Background:
Large amounts of detailed electronic health data are being collected. Reuse of these data has enormous potential for scientific discoveries that enables the improvement of healthcare systems’ effectiveness, efficiency, and quality of care. However, health data reuse should protect the privacy interests of the stakeholders (i.e., patients and healthcare providers) and promote public good through research. This is particularly challenging when the data are distributed across several data custodians.
Objective:
This paper aims to give an overall overview of existing privacy-preserving techniques for distributed data reuse and their practical applications.
Methods:
We searched for review papers that are focused on privacy-preserving techniques for different stages of distributed data reuse, such as creating dataset that satisfy a given criteria, analyzing the dataset, and releasing statistical results. We analyzed the identified techniques in terms of privacy, data utility, efficiency, and scalability. Practical uses of the techniques are also discussed when there is actual use.
Results:
Several privacy-preserving data reuse techniques have been identified. The techniques are developed for different stages of distributed data reuse based on de-identification, secure multi-party computation (SMC), or a combination of these two building blocks. Different combinations of the techniques need to be applied for the whole stages of distributed data reuse. Some of the surveyed techniques protect the privacy of data custodians in addition to individuals. The main challenge for de-identification based data reuse techniques is making a balance between utility and privacy. Whereas, efficiency and scalability are the main challenges for SMC based techniques.
Conclusions:
Enormous progress has been made towards making privacy-preserving reuse of distributed data possible. However, there are only few practical uses of the available techniques. the problem of distributed data reuse also requires governance, legal, and ethical frameworks, as well as the technical solutions. It is not clear whether consent, data-use agreement, and ethics review are required for practical uses of the techniques.
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