On his first day in office, President Biden took a significant step by signing an executive order that tasked federal agencies and White House offices with assessing barriers to racial equity and initiating efforts to rectify inequitable policies affecting individuals and communities of color. One such barrier, which has been a growing concern among researchers and public policymakers, is statistical data privacy (or statistical disclosure control) methods that provide researchers or policymakers access to data while preserving participants’ privacy often do not explicitly consider racial equity.

Methods such as data suppression, the addition of random noise under differential privacy, or the generation of synthetic data strive to strike a balance between the need for accurate information and privacy considerations. However, all these approaches involve a utility-risk tradeoff that can have equity implications for different racial groups. Without considering equity, researchers run the risk of unintentionally perpetuating harm by disproportionately distributing either the privacy risks or the utility of the information obtained from the data.

The following represents my work at the intersection of data privacy, equity, and public policy, as I strive to address these critical issues and promote a more equitable approach to data protection and governance.

Related Papers

• Bowen, CMK. & Snoke, J. (2023) “Do No Harm Guide: Applying Equity Awareness In Data Privacy Methods.” Urban Institute.
  • Click here to view the article on the project landing page (open-access).

Related Blogs

• I wrote a blog on the Urban Wire with my colleagues titled, To Advance Racial Equity, Releasing Disaggregated Data while Protecting Privacy Will Be Key
• A blog I co-authored on the Urban Wire about the Presidential Executive Order, How the Federal Government Can Use Data to Make the Most of the Executive Order on Racial Equity
• I wrote a blog on the Urban Wire titled, Will the Census’s Data Privacy Efforts Erase Rural America?

Within the extensive field of statistical data privacy literature, both differential privacy and data synthesis, and their integration, have gained significant popularity as solutions for releasing analytically valuable data while protecting individual privacy. It’s crucial to recognize that there is no methodological “silver bullet” that applies to all data. Therefore, ongoing development and refinement of differentially private methods and data synthesis techniques remain essential.

Below, I present my work in the development and practical application of differentially private and synthetic data methods to real-world datasets.

Balancing data utility against data privacy risks is a complex task. Many individuals seeking an answer to this question expect a one-size-fits-all utility or disclosure risk metric that perfectly assesses the quality of any data or statistic released under a privacy-preserving method or technology. But, such a metric doesn’t exist.

Below, I outline my work in evaluating the efficacy of different statistical data privacy methods and offer insights into how we should approach the delicate balance between privacy and utility.

The following represents my efforts in introducing data synthesis, differential privacy, and various statistical data privacy methods and technologies to a wider audience, including the scientific and non-technical communities.