Sharing privacy-sensitive access to neuroimaging and genetics data

A review and preliminary validation

Anand Sarwate, Sergey M. Plis, Jessica A. Turner, Mohammad R. Arbabshirani, Vince D. Calhoun

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the "small N" problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries-the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.

Original languageEnglish (US)
JournalFrontiers in Neuroinformatics
Volume8
Issue numberAPR
DOIs
StatePublished - Apr 7 2014

Fingerprint

Neuroimaging
Privacy
Computational methods
Data mining
Brain
Information Dissemination
Statistics
Derivatives
Data Mining
Plague
Confidentiality
Dermatoglyphics
Genomics
Genetic Markers
Genetics
Growth

All Science Journal Classification (ASJC) codes

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

Keywords

  • Collaborative research
  • Data integration
  • Data sharing
  • Neuroimaging
  • Privacy

Cite this

Sarwate, Anand ; Plis, Sergey M. ; Turner, Jessica A. ; Arbabshirani, Mohammad R. ; Calhoun, Vince D. / Sharing privacy-sensitive access to neuroimaging and genetics data : A review and preliminary validation. In: Frontiers in Neuroinformatics. 2014 ; Vol. 8, No. APR.
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Sharing privacy-sensitive access to neuroimaging and genetics data : A review and preliminary validation. / Sarwate, Anand; Plis, Sergey M.; Turner, Jessica A.; Arbabshirani, Mohammad R.; Calhoun, Vince D.

In: Frontiers in Neuroinformatics, Vol. 8, No. APR, 07.04.2014.

Research output: Contribution to journalArticle

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