Data De-Identification Toolkit

Authors

  • Aaron Kite-Powell MIT Lincoln Laboratory, Lexington, MA, United States
  • Kelly Moran MIT Lincoln Laboratory, Lexington, MA, United States

DOI:

https://doi.org/10.5210/ojphi.v6i1.5066

Abstract

Developing effective data-driven algorithms and visualizations for disease surveillance hinges on the ability to provide application developers with realistic data. However, the sensitivity of the data creates a barrier to its distribution. We have created a tool that assists data providers with de-identifying their data in preparation for sharing. The functions in the tool help data providers comply with the HIPAA "Safe Harbor" de-identification standard [1] by removing or obscuring information such as names, geographic locations, and identifying numbers.

Author Biography

Kelly Moran, MIT Lincoln Laboratory, Lexington, MA, United States

Kelly Moran is a member of the Informatics and Decision Support Group at MIT Lincoln Laboratory. She is currently pursuing a Master of Science in Computer Science with a focus on machine learning. She is particularly interested in the application of data mining and natural language processing techniques to the field of medical informatics.

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Published

2014-03-09

How to Cite

Kite-Powell, A., & Moran, K. (2014). Data De-Identification Toolkit. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5066

Issue

Section

Oral Presentations