PDF - Differential Risk-Based Anonymization - ON-167

Preferred Disciplines: Computer Science, Engineering, Applied Mathematics/Statistics (postdoc)
Company: Privacy Analytics Inc (an IQVIA company)
Project Length: 3 years (9 units)
Desired start date: January 2019
Location: Ottawa, ON
No. of Positions: 1
Preferences: Language: English

About the Company: 

A global authority in data anoymization, providing training, professional services, and software products

Project Description:

A risk-based approach to anonymization includes an assessment of the risk that an attack to reveal or uncover personal information will be realized, known as threat modelling, against the risk that an attack on the data will be successful (e.g., a re-identification). We wish to incoroporate the provable guarantees of differential privacy into this assessment of risk, to produce safe data in context of the environment in which it will be used. We also need adapt the methods of statistical disclosure control to such an updated approach.

Research Objectives:

  • Incorporate provable guarantees of differential privacy into contextual asessment of risk
  • Determine or develop the flavor of differential privacy that will allow for a contextual risk assessment
  • Build the appropriate tools and techniques for the sharing and release of microdata in a differential risk-based approach to data anonymization


  • Provable guarantees of differential privacy
  • Uncertainty analysis and expert judgement in risk analysis
  • Statistical disclosure control methods used to produce safe data—e.g., subsampling, generalization, suppression, noise addition, record swapping 

Expertise and Skills Needed:

    • Thourough knowledge and understanding of differential privacy, with a preference for candidates that have implemented solutions
    • Knowledge of, or willingness to learn, methods and approaches of statistical disclosure control
    • Knowledge and understanding of applied statistics and computer science
    • Practical and solutions driven for real-world application of anonymization
    • Strong programming skills for data analysis (e.g., R or Python)
    • Knowledge of high performance computing would be considered an asset (e.g., parallel programming or cluster computing)

    For more info or to apply to this applied research position, please

    1. Check your eligibility and find more information about open projects
    2. Interested students need to get the approval from their supervisor and send their CV to Mel Chaar at mchaar(a)mitacs.ca along with a link to their supervisor’s university webpage or by applying through the webform.