1. Anonymising quantitative data
Dr Sharon Bolton
UK Data Service
UK Data Archive, University of Essex
Anonymising Research Data workshop Dublin, 22 June 2016

2. The UK Data Service
Single point of access to wide range of social science data: ukdataservice.ac.uk
Funded by the ESRC to serve the academic community: training and guidance; UK Data Archive established 1967
Used by academic researchers and students; government analysts; charities; business; research centres; think tanks
Survey microdata; cohort studies; international macrodata; census data; qualitative/mixed methods data
Support and guide data creators, including disclosure review (anonymisation) and preparation for archiving

3. Protecting confidentiality: the ‘5 Safes’
Five guiding principles:
Safe people - educate researchers to use data safely
Safe projects - research projects for ‘public good’
Safe settings - SecureLab system for sensitive data
Safe outputs - SecureLab projects outputs screened
Safe data - treat the data to protect respondent confidentiality
For this session, we will concentrate (mostly) on Safe data

4. Data collection: planning
Explain to respondents what archiving entails and gain agreement for data sharing – informed consent
Think about disclosure risks before starting – what kind of information do you need to collect?
Direct identifiers include: names; addresses; telephone numbers; addresses; photos; (perhaps) IP addresses; do you really need them?
Unless explicit consent obtained for sharing, direct identifiers should always be removed from data

5. Anonymising data: indirect identifiers
Indirect identifiers include:
Sensitive information: health information/medical conditions; crime victimisation/offending; drug/alcohol use etc.
‘Less sensitive’ information: age/birth date; educational characteristics; employment details; religious affiliation; household size; geographic area
Look at demographics in combination (e.g. demographics + geographies)
Text/string variables – too detailed?

6. Anonymising indirect identifiers
Aggregate categories to reduce precision
Band ages, incomes, expenditure, etc. to disguise outliers
Use standard coding frames – e.g. SOC2010
Generalise meaning of detailed text
Document the changes you make
Talk to other researchers, archives, data services
Published guides:
UCD Research Data Management Guide
ONS Disclosure control guidance for microdata produced from social surveys

7. Anonymising data: new developments and tools
Statistical Disclosure Control (SDC) software is available:
mu-Argus
standalone software package recommended by Eurostat for government statisticians
software and manual:
R tool - SDCMicro (GUI)
Software, manual:
new documentation being developed by UK Data Service, working with R developers

8. Quiz 1: disclosive text in job title
Frequency
Valid Percent
nurse 73
73.0
carer for elderly man 1
1.0
hospital ward cleaner
social science researcher
head of dental practice 2
2.0
cleaner in electronics factory
Financial Director, Sunnyview Care Home, Colchester
general manager GP
Manager, Cotterill Village Stores
works in electronics factory
on benefits, not working
police officer
consultant, geriatric psychiatry
Reetired
retired
Retired
retirement
geography teacher
Teacher, music
Seondary school teeacher
unemployed
web designer
Total
100
100.0

9. Quiz 1: jobs coded with SOC2010
Job title: SOC2010
Frequency
Valid Percent
1131: Director, financial 1
1.0
1171: Manager, general
1190: Manager, retail
2231: Nurse 73
73.0
2426: Researcher
2215: Dentist 2
2.0
2211: Doctor, medical
3312: Officer, police
2314 Teacher, secondary 3
3.0
2137: Designer, web
6145: Carer
9139: Worker, factory
9233: Cleaner
Retired 4
4.0
Unemployed
Total
100
100.0

10. Quiz 2: detailed religion categories
Religious affiliation
Frequency
Valid Percent
1 Protestant 41
41.4
2 Anglican 4
4.0
3 Catholic 26
26.3
4 Muslim 8
8.1
5 Sikh 5
5.1
6 Jehovah's Witness 6
6.1
7 Methodist 1
1.0
8 Mormon
9 Baptist
10 Buddhist 3
3.0
11 None
12 No religion
13 Moravian
Total 99
100.0

11. Quiz 2: religion categories aggregated
Religious affiliation
Frequency
Valid Percent
1 Protestant 49
49.0
3 Catholic 26
26.0
4 Muslim 8
8.0
5 Sikh 5
5.0
6 Other religion 10
10.0
7 No religion 2
2.0
Total
100
100.0

12. Quiz 3: age in years Age in years Frequency Valid Percent 16 3 3.0 17
Frequency
Valid Percent 16 3
3.0 17 18 9
9.0 19 20
16.0 21 4
4.0 22 2
2.0 23 24 25 26 27 28 29 30 31 1
1.0 32 40 11
11.0 41 42 43 49 50 13
13.0 51 60 61 62 63 64
Total
100
100.0

13. Quiz 3: banded age Age (banded) Frequency Valid Percent 1 16-20 40
Frequency
Valid Percent 40
40.0 22
22.0 13
13.0 19
19.0 6
6.0
Total
100
100.0

14. Access control
Don’t over anonymise - find balance between protecting respondents’ confidentiality and maintaining research usability of data
Can’t fully anonymise data without removing all the useful detail? Go back to the 5 Safes – think about access control: Safe people, Safe settings, Safe outputs

15. Access control
At UK Data Service, data available under 3 access levels:
OPEN – open public access
SAFEGUARDED – downloadable, but use is traceable
Registered users only (agree not to try to identify any individual respondents)
Special agreements/licence: permission-only access; approved projects – usage agreed in advance
CONTROLLED – accredited users take a further training course
Access via on-site safe setting or virtual secure environment
(SecureLab)
Outputs disclosure-checked before publication

16. Anonymising quantitative data: summary
Informed consent
Think about level of detail needed before data collection
Remove direct identifiers
Check and treat indirect identifiers to reduce disclosure risk
Document your changes
Balance anonymisation with access control to preserve data usability

17. Questions? Guidance on anonymisation:
UCD:
UKDS:
Managing and Sharing Research Data book