1 — U.S. BUREAU OF LABOR STATISTICS • bl s.gov

Balancing 

Respondent Confidentiality 

and Data User Needs

Aaron E. Cobet

Consumer Expenditure Surveys

Microdata Users Workshop

July 22, 2020
2 — U.S. BUREAU OF LABOR STATISTICS • bl s.gov

What is the Issue?

 Conflicting goals 

 Maximize data access

 Protect respondents identity
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Why is Confidentiality Important? 

 Ensure trust of respondents for 

their cooperation

 It’s the law 
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What is Title 13? 

 U.S. Code: Title 13 allows the Census Bureau 

to take a survey and provides directives for its 

administration and enforcement.

 People who took the oath who wrongfully 

disclose information protected under Title 13 

are subject to a fine of up to $250,000 or up 

to 5 years in prison or both.

 Census and CE staff need Title 13 clearance. 
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Title 13 Training 

 CE staff gain access to internal data after

completing 2 steps: 

1. Pass a background check by Census

2. Take the Title 13 training 

 CE staff are required to annually retake 

Title 13 training
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Who Determines Disclosure Threats?

Disclosure Review Board 

of the Census Bureau
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How Could Microdata Reveal 

Respondents’ Identity? 

Unique data points

 Names 

 Addresses

 Extreme income 
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How to Protect Respondents’ 

Confidentiality?

 Census removes 

direct identifiers, 

e.g. names

 BLS suppresses 

indirect identifiers, 

e.g. high income

Conceal revealing information
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How to Conceal Indirect Identifiers?

 Topcode: Average numerical 

values above threshold

 Recode: Change item or CU 

characteristics 

 Suppress: Delete numerical value 

or delete entire record
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How do we Topcode?

 Determine critical value

 Average values exceeding critical value 

 Replace exceeding values with topcoded values
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Topcoding Example

 -

 100

 200

 300

 400

 500

 600

0 2 4 6 8 10 12 14 16 18 20
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Topcoding Example

 -

 100

 200

 300

 400

 500

 600

0 2 4 6 8 10 12 14 16 18 20

Critical value 
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Topcoding Example

 -

 100

 200

 300

 400

 500

 600

0 2 4 6 8 10 12 14 16 18 20

Extreme values 
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Topcoding Example

 -

 100

 200

 300

 400

 500

 600

0 2 4 6 8 10 12 14 16 18 20

Topcoded values 
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How to Determine Critical Values?

 Critical value is any value by a consumer 

unit above the specified percentiles: 

Expenditures: 99.5 %

Income: 97.0 % 
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How do we Recode?

 Find revealing metadata

 Determine method: 

 Generalize information

 Change information

 Replace original metadata with recoded 

metadata
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Re-coding: Generalize Information

From Toyota Corolla 1999 

To Toyota 1990s 
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Re-coding: Change information

 Change states to comparable states

Delaware

New Jersey

Hi
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How to Conceal Indirect Identifiers?

 Top-coding: Provide average of 

expenditures above a threshold 

 Re-coding: Change item or CU 

characteristics

 Suppression: Delete numerical data or 

entire record
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Suppression

 Erase aspect of the record

Example: State suppression

Example: Boat purchase 

 Exclude entire record

Example: Airplane purchase
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Reverse Engineering

5 = 3 + X 

What’s X?
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How to Prevent Reverse Engineering?

Prevent users to deduce protected 

information within files and across files

1. Find protected values

2. Protect them in all locations 

3. Protect related values 
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Reverse Engineering: Within File

 Income = Wages + taxes 

 1000 = 800 + 200

 1000 = 750 + 200

 950 = 750 + 200 

 Critical value: 700 

 Topcode value: 750

Wages 

exceeds

the critical 

value
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Reverse Engineering: Within File

 Income = Wages + taxes 

 1000 = 800 + 200

 1000 = 750 + 200

 950 = 750 + 200 

 Critical value: 700 

 Topcoded value: 750

Wages 

match 

the 

topcoded

value
29 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bl s.gov bl s.gov

Reverse Engineering: Within File

 Income = Wages + taxes 

 1000 = 800 + 200

 1000 = 750 + 200

 950 = 750 + 200 

 Critical value: 700 

 Topcode value: 750

Wages 

and taxes 

match 

the 

income
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Reverse Engineering: Across Files

 Income: Topcoded income in FMLI

Topcode associated UCCs in ITBI 

 Expenditure: Topcoded expenditures 

in EXPN and FMLI

Topcode associated UCCs in MTBI
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How Do We Document?

Flag values

T: Topcoded value 

D: Valid value, unadjusted
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Impact of topcoding

 CE topcodes few observations

 Most affected data slices:

Geographic data non-self representing cities

Income for high earners 
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Impact of suppression of 

geographic variables, Percent

 60 % of PSU 

suppressed

 4 % of regions 

suppressed

Source: FMLI and FMLD files for 2018. 

0

10

20

30

40

50

60

70

PSU State Division Region
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Need More Data?

 Visiting researcher program

 Access to pre-topcoded CE microdata

 Requires application process

 www.bls.gov/rda/home.htm
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Additional Information

 Protection of respondent confidentiality 

(www.bls.gov/cex/pumd_disclosure.htm)

 PUMD Getting Started Guide 

(www.bls.gov/cex/pumd-getting-started-guide.htm)

 Title 13 

(www.census.gov/history/www/reference/privacy_c

onfidentiality/title_13_us_code.html)
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Aaron Cobet

Senior Economist, Consumer Expenditure Surveys

(202)-691-5018

Cobet.Aaron@bls.gov

Thank you!