Term: Spring 2022

Date: January 28th, 2022

Time: 12:00pm - 1:30pm

Location: Online Only

Instructor: Kory Trott

Presented By: Scholarly Integrity and Research Compliance (SIRC)

Description:

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.