The OpenDP project at Harvard engages collaborators in academia, industry, and government to build trustworthy, open-source software tools for privacy-protective statistical analysis of sensitive personal data

Human mobility data has been used extensively to inform and fit models of disease transmission. While these data can be useful in these models, they bring up questions of identifiability and privacy. Differential privacy is a potential solution to this issue; however, more research is needed to better understand the effects of application of DP on the outputs of interest in epidemiological models. 

Work in Progress 

  1. An evaluation of varying levels of differential privacy and the outputs of epidemiological models.  
  2. A standard framework to apply differential privacy on data that may be used in human mobility research.  

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