# Presentation: 808067

## Overview of Statistical Disclosure Methodology for Microdata

- Laura Zayatz
- Census Bureau
- laura.zayatz@census.gov

**BTS Confidentiality Seminar Series, April 2003**

## Microdata

- Respondent level data
- Each record represents one responding household or person
- Usually demographic data

## Microdata Disclosure Risk

- High visibility records
- Linkable external files

## Addressing Risk

- Can we measure it? (file level – function of ever-changing external database environment)
- Getting specific (record level)
- Reduce the amount of information
- Distort the information

## Making the Data Safe

- Remove obvious identifiers
- Set a geographic threshold
- Know external data
- Know your data
- Look for problems

## Look for Problems:External Files

- Get to know external files
- State and local government agencies
- Other federal agencies
- Private firms ($)
- Perform reidentification experiments
- Hunt and peck
- Data fusion

## Look for Problems:Your File

- Special uniques
- 2 way cross tabulations of all variables
- Work your way up
- Variables that you know exist on external databases (even if you cannot access them)

## Be Careful with ......

- Geographic detail
- Contextual variables/Sampling information
- Establishment data
- Longitudinal data (life events often generate records on external files)
- Administrative data
- Data that will also be published in the form of tables at smaller geographic levels

## When You Find a Problem.....

- Reduce risk by reducing the amount of information
- Reduce risk by perturbing the data

## Reducing the Amount of Information

- Delete a variable
- Recode a categorical variable into larger categories (perhaps using thresholds)
- Recode a continuous variable into categories
- Round continuous variables
- Use top and bottom codes (provide means,...)
- Use local suppression
- Enlarge geographic areas

## Perturbing the Data

- Noise addition
- Swapping
- Rank Swapping
- Blanking and imputation
- Microaggregation
- Multiple imputation/modeling to generate synthetic data

## Census 2000 Public Use Microdata Samples (PUMS) --- Decrease in Detail

- Internal reidentification study looking at all products (microdata and tables) from 1990 census
- Increased concern about external data linking capabilities

## The Files

- 17% of households receive the long form
- Maximum of 6% of the population appears on PUMS
- 2 mutually exclusive files
- 5% state file, PUMAs contain 100,000 people, less variable detail, 2003 release
- 1% characteristics file, SuperPUMAs contain 400,000 people, same variable detail, 2003 release

## Changes for the 5% and the 1% Files

- Round all dollar amounts
- $1-7 = $4
- $8-$999 round to nearest $10
- $1,000-$49,000 round to nearest $100
- $50,000+ round to nearest $1,000

## Changes for the 5% and the 1% Files

- Round departure time
- 2400-0259 in 30-minute intervals
- 0300-0459 in 10-minute intervals
- 0500-1059 in 5 minute intervals
- 1100-2359 in 10-minute intervals

## Changes for the 5% and the 1% Files

- Noise added to ages of people in households with 10 or more people
- Ages must stay within certain groupings
- Blank original ages
- New ages generated from a given distribution of ages in that grouping

## Small Amount of Additional Noise

- Certain characteristics of small, unusual subgroups of people and housing units
- Vulnerable to disclosure via publicly available datasets
- Not disclosing the details
- Resulted from reidentification studies

## Changes for the 5% File OnlyCategories Must Have 10,000 People Nationwide

-                           1990                  2000
- Language     	305			74
- Ancestry		292			143
- Birthplace	312			167
- Tribe		27			23
- Hispanic O.	48			29
- Occupation	506			443

## Multiple Races

- White
- Black
- American Indian or Alaska Native
- Asian:  Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Other Asian
- Native Hawaiian or Pacific Islander:  Native Hawaiian, Guamanian or Chamorro, Samoan, Other Pacific Islander
- Some Other Race

## Race on the PUMS

- Yes/No for each major race grouping (6)
- Designation of 1 of about 70 single race groups (includes some write-ins)
- Designation of multiple race groups
- At least 10,000 (5% file) and 8,000 (1% file) people nationwide

## Data Swapping

- 5% and 1% files
- Swapping of “special uniques” --- high risk records
- Pairs of households that agree on certain demographic characteristics but are in difference geographic areas are swapped across PUMAs and SuperPUMAs

## Issues that Have Not Changed

- Topcoding (half-percent/three-percent rule)
- Property taxes (categorization)

## Conclusion

- Know your data
- Know external data
- Proactively look for problems
- If possible, if you find a disclosure problem, let users help you choose the best method for reducing risk of disclosure

## Thank You for Coming

- laura.zayatz@census.gov
- 301-457-4955