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Seminar on Statistical Disclosure Control

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Statistical Disclosure Control has been an issue at the forefront of privacy for many years. The emphasis on open science and reproducible & replicable research has precipitated the release of both raw and summary data on publicly accessible portals. Yet, not all of those releasing such data are aware of re-identification risks. Government agencies and academic researchers have advanced initial methodological and policy work in this area ultimately culminating in NIST standards, journal articles, and books which address re-identification risk. The current state of statistical disclosure control (SDC) methodologies goes well beyond Safe Harbor from HIPAA and other best practices aimed at reducing the likelihood of data re-identification. The more advanced SDC techniques from these sources should be employed to reduce the risk in many cases.

This seminar will introduce you to the concepts of data re-identification, quasi-identifiers, and sensitive values from SDC. We will provide guidance on conducting data re-identification risk assessments and the accompanying SDC techniques used to assess and mitigate this risk. These include concepts like k-anonymity, l-diversity, household/cluster risk, etc. The trade-off between data utility and re-identification will be discussed in the context of methods such as suppression, perturbation, etc. that are used to mitigate the risk. Available tools for implementing these analyses will be highlighted.

College: Research and Innovation
Department: SIRC
Section: RICP
Term: Spring 2022
Start Date/Time: January 27th, 2022 7:00 PM
End Date/Time: January 27th, 2022 7:00 PM

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