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Maintained Individual Data Distributed Likelihood Estimation (MIDDLE).

Steven M Boker1, Timothy R Brick2, Joshua N Pritikin1

  • 1a Department of Psychology, University of Virginia.

Multivariate Behavioral Research
|December 31, 2015
PubMed
Summary
This summary is machine-generated.

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) enables private data sharing for research. This approach allows statistical modeling and hypothesis testing without researchers accessing individual participant data, enhancing privacy and efficiency.

Keywords:
data sharing, distributed computation, ecological momentary assessment, privacy, smart phones

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Area of Science:

  • Behavioral science
  • Social science
  • Health science

Background:

  • Traditional research methods often involve central data collection, raising privacy concerns.
  • Participant data ownership and control are increasingly important ethical considerations in research.

Purpose of the Study:

  • To introduce and describe the Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) paradigm.
  • To highlight the benefits of MIDDLE for participant privacy, data security, and research efficiency.

Main Methods:

  • MIDDLE utilizes distributed likelihood estimation, where individual data remains on participant devices.
  • Objective functions and parameters are sent to participants' devices for local likelihood calculation.
  • Only aggregated likelihood values are returned to a central optimizer for model fitting.

Main Results:

  • MIDDLE ensures significantly greater privacy for participants compared to traditional methods.
  • The approach offers automatic management of participant consent (opt-in/opt-out).
  • MIDDLE reduces costs for researchers and funding bodies, leading to faster results.

Conclusions:

  • MIDDLE represents a novel and effective paradigm for conducting research in behavioral, social, and health sciences.
  • This method enhances participant privacy, data control, and research efficiency.
  • MIDDLE facilitates secure, longitudinal data linkage across multiple studies when participants opt-in.