cbowen@urban.org www.clairemckaybowen.com @ClaireMKBowen

Claire McKay Bowen, Ph.D

Principal Research Associate | Statistical Methods Group Lead
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▪ Motivation and Background

What inspired this guide?

How did we approach the study?

▪ Defining Equity

What are the different ways we 

define equity in the context of 

privacy?

▪ Takeaways and Concluding Thoughts

Where do we go from here?

Overview
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Why write this guide?
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▪ Lots conversations about equity and privacy

(e.g., Biden’s Executive Orders, CNSTAT 

reports, Evidence Act)

▪ Less conversations about equity in privacy

▪ Caveats about our guide:

➢ High level work, lots of qualitative work

➢ Exploratory and hopefully foundation laying

➢ Recommendations are aspirational and will 

take a lot of work

Motivation
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▪ Volume 4 of the Tableau funded 

Do No Harm Guide series

▪ Target audience are those with 

some familiarity with statistical 

data privacy (SDP)

▪ Guide includes: ▪ Literature review, mostly CS papers ▪ 9 subject matter expert interviews ▪ 3 illustrative examples, showing the 

impacts of various SDP methods

Background
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▪ Volume 4 of the Tableau funded 

Do No Harm Guide series

▪ Target audience are those with 

some familiarity with statistical 

data privacy (SDP)

▪ Guide includes: ▪ Literature review, mostly CS papers ▪ 9 subject matter expert interviews ▪ 3 illustrative examples, showing the 

impacts of various SDP methods

Background
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How do we define equity?
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1. Disparities in the privacy loss and utility

2. Definitions and preferences of privacy loss and utility

3. Defining groups and representation

4. Resource allocation

5. Related processes

6. Misalignment with other efforts

7. Equity in the research field

What are the equity concepts within SDP?
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1. Disparities in the Privacy Loss and Utility
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2. Definitions and Preferences of Privacy and Utility

“To what extend do the privacy protections we provide 

through some system correspond to people’s real-world 

concerns and potential harms from breaches of privacy 

or confidentiality?”

– Adam Smith, Boston University

▪ Various groups may seek to define privacy/utility 

differently that best fits their needs and expectations.

▪ These groups may have different thresholds of privacy

and utility.

▪ How can groups place themselves in equally satisfactory positions?
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3. Defining Groups and Representation

“If everyone in the United States were carbon 

copies of one another, we wouldn’t have a lot of 

discussions about utility or equity.”

– Rolando Rodriguez, U.S. Census Bureau 

▪ How do we define groups in the data?

▪ Who represents their interests?

▪ How do we handle the sample size constraints?
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4. Resource Allocation

“Most [discretionary requests] are turned down. The 

people that work [at the statistical agency] always 

want to do these things, but again it’s the timing, the 

resources...most of them they feel like they can’t.”

– Daniell Toth, Bureau of Labor Statistics

▪ How do we balance agency mandated information releases vs. 

discretionary requests?

▪ How do we provide access to restricted data?

▪ How do we train people on how to properly use the data?
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4. Resource Allocation

“I’m talking about students for example...

that can’t afford hiring a stat analyst [who] 

has the skills to do some of the data science kind 

of stuff. How much do you help these people be 

successful in their job? ... How far do you go with 

this to help someone out who can’t get their code 

straightened out or can’t write well?”

– Tom Krenzke, Westat
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5. Equity in Related Processes

“I think every county should have the right ability 

to analyze the statistics we are preparing and be 

useful.”

– Mauricio Ortiz, Bureau of Economic Analysis

▪ We need to remember that SDP is only a part of a much larger 

pipeline, where we should minimize inequalities in data quality.

▪ What is an adequate starting place?
6. Misalignment with other Equity Efforts

“If [equity] isn’t considered in the design of the study 

for that small subgroup, then it’s not the main 

purpose of the study. Other parts will take priority. 

Afterall, what was the study designed for?”

– Saki Kinney, RTI

▪ We should consider including more granular data collection and 

incorporating privacy conversations early.

▪ We should not treat equity and privacy as separate studies.

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7. Equity in the Research Field

“Certain research methods are a priori seen as 

legitimate or illegitimate depending on the 

community. But pluralist viewpoints work for 

other fields so we should be open to that and to 

more collaboration as well.”

– Aleksandra Slavković, Penn State

▪ SDP researchers should collaborate more and incorporate other 

perspectives.

▪ This will increase openness to different approaches and 

acknowledgment of previous work.
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Where do we go from here?
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An Aspirational Workflow for Equitable SDP

Step 6: Document and publish each step of the process

Step 5: Choose SDP implementation that best satisfies definitions, preferences, and constraints

Step 4: Communicate constraints to representatives and decision-makers

4a: Use metrics and visuals for group-level 

tradeoff curves

4b: Detail technical shortcomings (group size 

constraints)

Step 3: Make determinations with representatives and decision-makers

3a: Define statistical utility and privacy loss 3b: Choose group-level preferences for statistical 

utility and privacy loss thresholds

Step 2: Identify appropriate representatives for the groups

Step 1: Identify groups in the data
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▪ Do not treat equity as a separate field of 

study.

▪ Consider literature and perspectives 

from fields outside of your own.

▪ Estimate separate privacy loss-utility 

curves for groups.

▪ Work with groups represented in your 

data.

▪ There is no methodological silver bullet.

Concluding Thoughts
Contact Me

@ClaireMKBowen

cbowen@urban.org

www.clairemckaybowen.com

/in/bowenclaire