• Institute Intelligence and Data



    • Title of the research project
    Statistical Analysis of Synthetic Data Sets Satisfying Differential Confidentiality
    Description

    Data sharing is often limited by privacy issues. This is very common in particular for health datasets, given the inherent sensitivity of this type of data. When sharing of the original dataset is not possible, one method that can be used is to generate a synthetic dataset, which contains as much statistical information as possible from the original dataset, but which provides data on false individuals in order to protect the confidentiality of respondents.

    Title of the research project
    Statistical Analysis of Synthetic Data Sets Satisfying Differential Confidentiality
    Description

    Data sharing is often limited by privacy issues. This is very common in particular for health datasets, given the inherent sensitivity of this type of data. When sharing of the original dataset is not possible, one method that can be used is to generate a synthetic dataset, which contains as much statistical information as possible from the original dataset, but which provides data on false individuals in order to protect the confidentiality of respondents.

  • Leila Nombo

    Ph.D. candidate
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Anne-Sophie Charest
    Co-researcher
    Venkata Manem
    Start date
    Title of the research project
    Statistical Analysis of Synthetic Data Sets Satisfying Differential Confidentiality
    Description

    Data sharing is often limited by privacy issues. This is very common in particular for health datasets, given the inherent sensitivity of this type of data. When sharing of the original dataset is not possible, one method that can be used is to generate a synthetic dataset, which contains as much statistical information as possible from the original dataset, but which provides data on false individuals in order to protect the confidentiality of respondents. One way to ensure that these synthetic data effectively protect respondents is to use differential confidentiality, a rigorous measure of disclosure risk. 
    This project is interested in how to analyze these synthetic datasets to obtain valid statistical results, as traditional methods of inference need to be modified to account for the variability added by the generation of the synthetic dataset.
     

    • Title of the research project
    Intraoperative analgesic treatment decisions based on the NOL index: contribution of a data-based approach to improve accuracy and relevance
    Description

    This research project is based on the analysis of massive data on the NOL index and other intraoperative clinical parameters used by anesthesiologists during surgery. These parameters help them make analgesic treatment decisions in a non-communicating patient under general anesthesia and in whom it is impossible to assess pain and analgesic needs by standard questionnaires performed on awake patients. 
    First, the objective is to interpret the values of this index in relation to the decisions made by the clinician. 

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    Featured project

    Intraoperative analgesic treatment decisions based on the NOL index: contribution of a data-based approach to improve accuracy and relevance

    Student member : Marzieh Ghiyasinasab

    This research project is based on the analysis of massive data on the NOL index and other intraoperative clinical parameters used by anesthesiologists during surgery. These parameters help them make analgesic treatment decisions in a non-communicating patient under general anesthesia and in whom it is impossible to assess pain and analgesic needs by standard questionnaires performed on awake patients. 
    First, the objective is to interpret the values of this index in relation to the decisions made by the clinician. 

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