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ANONYMISATION
AND SOCIAL
RESEARCHRuth Geraghty
Data Curator on the CRNINI-PEI Research Initiative
Children’s Research Network for Ireland and
Northern Ireland
Anonymising Research Data workshop, University College Dublin, 22nd June
2016
www.childrensresearchnetwork.org
THIS PRESENTATION
Part 1: The role of anonymisation in social research
• Why focus on anonymisation?
• What legal requirements should we be aware of?
• What ethical considerations should we think about?
Part 2: Incorporating anonymisation into your research design
• Before you start your research
• Assessing sensitivity before sharing
• Set a protocol or procedure for anonymisation
• Choose a suitable access option
Part 3: Quick start tips
• General tips to get you started
PART 1: THE ROLE OF
ANONYMISATION IN SOCIAL
RESEARCH
Legal and ethical
considerations
WHY FOCUS ON ANONYMISATION?
IN THE MIDST OF A CHANGE IN PRACTICE
TOWARDS SHARING DATA
Context of the recent past:
RACcER: Re-use and Archiving of Complex Community-Based
Evaluation Research (IQDA and Tallaght CDI)
• 2009-2010
• 30 individuals from academic, policy and practice setting in ROI and
NI
AT THE TIME OF INTERVIEW ARCHIVING WAS A VERY NEW CONCEPT FOR MOST OF
THE RESPONDENTS AND MANY FELT THAT THEY, “WOULDN'T EVEN BE COMFORTABLE
ASKING SOMEONE TO AGREE TO ARCHIVE THE INFORMATION” BECAUSE THEY,
“DON’T KNOW ENOUGH ABOUT WHAT IMPLICATIONS [ARCHIVING] WOULD HAVE FOR
[PARTICIPANTS]” (Geraghty 2014, pg. 9).
VERY RECENT DEVELOPMENTS IN
IRELAND WITH POSITIVE IMPLICATIONS
FOR ARCHIVING AND SHARING
June 2015: Launch of Digital Repository of Ireland in as national repository for
humanities and social science research data – IQDA a founder member – digital
infrastructure and robust policy for sharing and making available of Irish social science
data – DRI awarded Data Seal of Approval in 2015
Autumn 2015: Establishment of permanent Data Manager role in ISSDA (UCD Library)
May 2016: Launch of HRB report Proposals for an Enabling Data Environment for Health
and Related Research in Ireland calls for the implementation of a data access, storage,
sharing and linkage (DASSL) model for health data in Ireland generated both by
administrative and research sources
Sep 2015 – July 2017: The Atlantic Philanthropies fund the CRNINI-PEI Research
Initiative to archive legacy research data from 50+ independent evaluations conducted
between 2004 – 2014 – this fund including dissemination of grants to promotes
secondary analysis
INDICATIVE OF A GROWING INTEREST IN ARCHIVING AND SHARING SOCIAL
SCIENCE DATA AND THE NEED THEREFORE FOR PRACTICAL GUIDANCE ON
HOW TO PREPARE RESEARCH DATA FOR SHARING
THREE STEPS TO SAFELY SHARING
RESEARCH WITH HUMAN SUBJECTS
“RESEARCH DATA – EVEN SENSITIVE AND CONFIDENTIAL DATA – CAN BE
SHARED ETHICALLY AND LEGALLY IF RESEARCHERS PAY ATTENTION, FROM
THE BEGINNING OF RESEARCH, TO THREE IMPORTANT ASPECTS:
• WHEN GAINING INFORMED CONSENT, INCLUDE PROVISION FOR DATA SHARING
• WHERE NEEDED, PROTECT PEOPLE’S IDENTITIES BY ANONYMISING DATA
• CONSIDER CONTROLLING ACCESS TO DATA”
(UK DATA ARCHIVE’S MANAGING AND SHARING DATA BEST PRACTICE,
pg. 23)
CONSENT ANONYMITY
ACCESS
CONTROL
ANONYMISATION
“WE USE THE TERM ‘ANONYMISED DATA’ TO REFER TO DATA THAT
DOES NOT ITSELF IDENTIFY ANY INDIVIDUAL AND THAT IS UNLIKELY
TO ALLOW ANY INDIVIDUAL TO BE IDENTIFIED THROUGH ITS
COMBINATION WITH OTHER DATA.” (ICO ANONYMISATION CODE OF
PRACTICE, pg. 6)
DIRECT: immediately identifiable information e.g. name
and address, photographs
INDIRECT: identifiable through combination of information
e.g. linked information
Anonymisation requires a ‘whole picture’ approach
ANONYMITY
BALANCE BETWEEN PROVIDING RICH
INFORMATION THAT IS USEFUL IN A RANGE
OF CONTEXTS WITH PROTECTING THE
PRIVACY OF INDIVIDUALS
Name: Enda Kenny
Date of birth: 24 April 1951
Education: B.A. Education,
University College Galway
Place of birth: Derrywash,
Islandeady, County Mayo
Current employment: An
Taoiseach and Minister for
Defence
Family status: Married to
Fionnuala O'Kelly, three
children
Name: @@Pat Keegan##
Age group: 50 - 65
Highest education:
Degree
Place of birth: West
region of Ireland
Current employment:
Politician
Family status: Married,
<5 children
Anonymisat
ion process
Combination
of
pseudonym,
aggregation
and redaction
results in data
that indicates
characteristics
of participant
but is not
specific
enough to
disclose
identity
Variable Case 1 Case 2
Name Viacheslav
Fesenko
Chris Fournier
DOB 18 April 1981 04 June 1987
Highest Ed. B.Ss. Sports
Science
B.Sc. Sports
Rehabilitation
Place of birth Kiev, Ukraine Lyon, France
Current
residence
Mullingar, Co.
Westmeath
Celbridge, Co.
Kildare
Sport Professional
basketball
player
Professional
basketball player
Months playing
with Irish team
18 36
Variable Case 1 Case 2
Name Case 01 Case 02
Age group 29 - 35 29 - 35
Highest Ed. Degree (sport
and fitness)
Degree (sport and
fitness)
Place of birth Europe (non
Irish)
Europe (non Irish)
Current
residence
Leinster Leinster
Sport Professional
basketball
player
Professional
basketball player
Months playing
with Irish team
18 36
Anonymisa
tion
processYou decide which information is really
key to your database and which
information you can afford to alter or
remove
In this case the really key information that is left
intact is their specific sport and their length of time
with Irish team. Useful info such as where they moved
to Ireland from and where exactly they are living and
working is retained using broader categories.
WHAT LEGAL REQUIREMENTS
SHOULD WE BE AWARE OF?
EUROPEAN AND DOMESTIC ACTS WITH
RELEVANCE TO STORING AND SHARING
RESEARCH DATAEUROPEAN
General Data Protection Regulation (Regulation (EU) 2016/679 and Directive (EU)
2016/680)
Replaces the European Data Protection Directive (95/46/EC)
Stronger regulation to take into account vast technological changes of the last 20
years – all member states have to transpose it into their national law by 6 May 2018
“It has also turned out to be fairly research-friendly as the most important special
provisions from the Directive are continued, clarified and strengthened. Amongst other
this applies to the possibility to process personal data based on other grounds than
consent.” (Big Data Europe blog, 6th June 2016).
DOMESTIC
REPUBLIC OF IRELAND
Data Protection Act 1988 and Amendment Act 2003
UNITED KINGDOM
Data Protection Act 1998
‘PERSONAL DATA’ LEGAL DEFINITION
UK Data Protection Act 1998
“personal data” means data which relate to a living individual who can be
identified —
(a) from those data, or
(b) from those data and other information which is in the possession of, or is
likely to come into the possession of, the data controller,
and includes any expression of opinion about the individual and any indication of
the intentions of the data controller or any other person in respect of the
individual
ROI Data Protection Act 1988 (2003)
“personal data” means data relating to a living individual who can be identified
either from the data or from the data in conjunction with other information in the
(26) The principles of data protection should therefore not apply to anonymous
information, namely information which does not relate to an identified or
identifiable natural person or to personal data rendered anonymous in such a
manner that the data subject is not or no longer identifiable. This Regulation does
not therefore concern the processing of such anonymous information, including for
statistical or research purposes.
ANONYMISATION IN THE CONTEXT OF NEW
EU GENERAL DATA PROTECTION REGULATION
ANONYMISATION IN THE CONTEXT OF ROI
DATA PROTECTION ACT 1988 (AMENDMENT ACT
2003)
In the context of anonymisation:
“IRREVOCABLE ANONYMISATION OF PERSONAL DATA PUTS IT OUTSIDE DATA PROTECTION REQUIREMENTS
AS THE DATA CAN NO LONGER BE LINKED TO AN INDIVIDUAL AND THEREFORE CANNOT BE CONSIDERED
TO BE PERSONAL DATA.” (DPC DATA PROTECTION GUIDELINES ON RESEARCH IN THE HEALTH SECTOR
2007, PG. 9)
Existing provision for archiving data for research purposes:
Section 2. Collection, processing, keeping, use and disclosure of personal data
(c) the data— (iv) shall not be kept for longer than is necessary for that purpose or those
purposes
(5) (a) Paragraph (c) (iv) of the said subsection (1) does not apply to personal data kept for
historical, statistical or research purposes if the data are not used in such a was that damage or
distress is, or is likely to be, caused to any data subject.
ANONYMISATION IN THE CONTEXT OF
UK DATA PROTECTION ACT 1998
DATA PROTECTION LAW DOES NOT APPLY TO DATA RENDERED
ANONYMOUS IN SUCH A WAY THAT THE DATA SUBJECT IS NO LONGER
IDENTIFIABLE (INFORMATION COMMISSIONERS OFFICE ANONYMISATION
CODE OF PRACTICE, pg. 6)
Part iv of the Data Protection Act 1998 on exemptions
UK Anonymisation Network (UKAN) set up in 2012 as a means of establishing best
practice in anonymisation with funding by the Information Commissioner’s Office
(ICO) during for its first two years.
“Organisations that want or need to share, disseminate or publish their data for
secondary use are obliged under the DPA (1998), unless exempt, to transform the
data in such a way as to render it anonymous and therefore no longer personal”
(UK Anonymisation Network website)
INDEPENDENT AUTHORITIES FOR DATA
PRIVACYRepublic of Ireland
Office of the Data Protection Commissioner
https://www.dataprotection.ie/
United Kingdom
Information Commissioners Office
https://ico.org.uk
Anonymisation code of practice:
https://ico.org.uk/media/1061/anonymisation-code.pdf
WHAT ETHICAL CONSIDERATIONS
SHOULD WE THINK ABOUT?
CHANGING LANDSCAPE OF ETHICS IN TERMS
OF SHARING DATAIn the past clause to destroy data after a certain period as
an ethical commitment to protect individuals from harm
Recent developments that are challenging existing codes for research ethics
o Research with a central commitment to secondary analysis e.g. Growing Up in
Ireland the National Longitudinal Study of Children
o ‘New data’ types (objective rather than self-reported) e.g. harvested social
media data, research data linked with other information sources
o New approaches to analysis e.g. big data analysis, meta analysis and data
mining
o Research grants that promote re-using data that already exists (‘value for
money’ discourse)
o Research grants that promote archiving and sharing as an aspect of data
management plan (‘good science’ discourse)
o Open data and data democracy
o Digital dissemination platforms e.g. public data archives, web-access
INTERACTION BETWEEN ETHICS AND
LAW IN REGARD TO DATA
PROTECTION
Ethics:
moral
judgement,
what ought
to be done
Law:
codified
rule, what
must be
done
Data
protection
“RESEARCH ETHICS COMMITTEES (RECS)
OPERATE AT THE INTERFACE BETWEEN THE
LEGAL SYSTEM AND ETHICAL
FRAMEWORKS… SIGNING OFF ON
RESEARCH PROPOSALS BY A RESEARCH
ETHICS COMMITTEE MAY BE LINKED TO
INDEMNIFYING THE RESEARCHER AGAINST
ANY LEGAL ACTION ARISING FROM THE
RESEARCH."
(DASISH REPORT ABOUT NEW IPR
CHALLENGES 2013, pg. 10)Key aspects of ethics
• ‘Do no harm’ principle
• Professional integrity, reputation of the discipline
• Researchers position becomes relevant – pluralism is
a challenge
ETHICS AND ANONYMISATION
Ethical assessment tends to be more complex and nuanced than rule of law
For example: Case of the request for deceased mother’s transcript
ROI Data Protection Act 1988 (2003) definition of “personal data” means data
relating to a living individual
“WHERE PATIENT DATA IS ANONYMISED, THERE IS NO NEED FROM A DATA PROTECTION
PERSPECTIVE TO SEEK THE CONSENT OF PATIENTS FOR THE USE OF THE DATA FOR RESEARCH
AND CLINICAL AUDIT PURPOSES. THERE MAY, OF COURSE, BE ETHICAL CONSIDERATIONS IN
SOME CASES BUT THESE ARE OUTSIDE THE SCOPE OF THESE GUIDELINES” (DPC DATA
PROTECTION GUIDELINES ON RESEARCH IN THE HEALTH SECTOR 2007, PG. 9)
CASE STUDY: CRNINI-PEI RESEARCH INITIATIVE
AT THE CHILDREN’S RESEARCH NETWORK FOR
IRELAND AND NORTHERN IRELAND
o 50+ independent evaluations funded by The Atlantic
Philanthropies between 2008 - 2014
o Legacy data of great value - public good principle
o Retrospective archiving - Biggest problem – was not planned
for
Legal requirement where consent is missing but anonymisation
carried out…
Resulting in the following flow chart…
CONSEN
T
FLOW CHART TO
ASSESS POSSIBILITY
OF ARCHIVING PEI
DATA
ANONYMIT
Y
ACCESS
CONTRO
L
CONSEN
T
PART 2: INCORPORATING
ANONYMISATION INTO YOUR
RESEARCH DESIGN
General principles
BEFORE YOU START YOUR
RESEARCH: CONSENT
GAINING ETHICAL APPROVAL AND
ROLE OF ANONYMISATION
Make your case to ethics review board for approval to do both:
(1) consent to participate in the research
(2) consent to archive/share their (anonymised) data following the research
Retrospective consent – it is possible!
“MOST [RESEARCH PARTICIPANTS] DO BELIEVE THAT RESEARCH IS FOR THE PUBLIC GOOD,
AND THAT THEIR CONTRIBUTION WILL BE USED IN SOME WAY TO CREATE A BETTER
INFORMED SOCIETY, AND EVEN GO SOME WAY TOWARDS IMPLEMENTING POLICY CHANGES.
(Corti, Day & Backhouse 2000)
Retrospective ethical approval – also possible as landscape changes,
sensitivities diminish over time and apparent value of data increases
INFORMED CONSENT EXAMPLEAdapted from samples available from the UK Data Archive at
http://www.data-archive.ac.uk/create-manage/consent-ethics/consent?index=3
I agree to take part in the project. Taking part in the project will include being
Yes No
interviewed and recorded (audio or video)
I understand that my personal details such as phone number and address will
Yes No
not be revealed to people outside the project
I agree for the data I provide to be archived at [archive name] Yes
No
I understand that other authenticated researchers will have access to this data only
Yes No
if they agree to preserve the confidentiality of the information as requested in this formMake sure the agreement that you come to with the archive on access
replicates what you have committed to in the consent form
ASSESSING SENSITIVITY OF DATA
RISK ASSESSMENT
About managing risk rather than removal of all risk: “The risk of disclosure is not zero
(it should be negligible)” (UKAN website)
“ The [UK Data Protection Act 1998] does not require anonymisation to be completely
risk free – you must be able to mitigate the risk of identification until it is remote.” (ICO
Anonymisation Code of Practice, pg. 6
The value of discussing a decision with your team
The value of consulting the experts on archiving and sharing
No hard and fast rules – but some tools…
TOOL A: IQDA ASSESSING SENSITIVITY
LEVEL OF THE DATA
Two parameters
o identification
o harm
“IS THERE A RISK THAT THE TEXT WILL ALLOW THE RESEARCH PARTICIPANT TO BE IDENTIFIED?
IS THERE A RISK THAT THE TEXT WILL BE HARMFUL TO THE RESEARCH PARTICIPANT? FOR
EXAMPLE, MIGHT THE TEXT EXPOSE THE PARTICIPANT TO RIDICULE OR HAVE OTHER ADVERSE
CONSEQUENCES FOR THEM?
BY EXAMINING THESE TWO PARAMETERS IT IS POSSIBLE TO MAKE AN OVERALL ASSESSMENT OF
THE RISK LEVEL OF THE DATA” (IQDA AND TALLAGHT CDI BEST PRACTICE IN ARCHIVING
QUALITATIVE DATA PG. 6)
Risk of Identification Risk of Harm Sensitivity Level
Little Low Low
Some Low Medium
Any High High
Step 1. Assess the data in front of you
whether it is aggregate data or data with individuals
what the key variables are
what is likely to affect the risk level of your data such as sensitivity of your data
disclosiveness of your data (many variables for example)
who has access to the data
Step 2. Scenario analysis
other relevant external data sources
potential intruders
the likelihood of an attempt of attack on dataset
This information will help you establish relevant plausible scenarios for your data situation.
When you undertake a scenario analysis, you are essentially considering the how, who and why of a potential
breach
3. Testing the scenarios
TOOL B: UKAN ASSESSMENT OF DISCLOSURE RISK (FROM
SECTION 6 OF ONLINE TRAINING*)
* http://ukanon.net/ukan-resources/course/#0.1
SET A PROTOCOL FOR
ANONYMISATION (ESPECIALLY FOR
TEAM WORK)
ANONYMISATION IS ALTERING DATA
Keep track of changes during the process - The ‘changes file’
Note changes in the contextual materials
Team work
o Protocol very important
o Consistency across the way data is altered
o Check in regularly
WHEN BAD ANONYMISATION HAPPENS TO
GOOD DATA…Original: So my primary school was Skryne National School which was about 10 minutes from
my home in Dunsany. My best friends from day one were Karen, Mary and Sarah and in fact I
am still very good friends with Mary to this day. She lives in the same parish still with her
husband John and their son Cian.
Example A, too light: So my primary school was [name] National School which was about 10
minutes from my home in Dunsany. My best friends from day one were Karen, Mary and Sarah
and in fact I am still very good friends with Mary to this day. She lives in the same parish still
with her husband John and their son Cian.
Example B, too heavy: So my primary school was X which was about X minutes from my home
in X. My best friends from day one were X, X and X and in fact I am still very good friends with
X to this day. She lives in the same parish still with her husband X and their X X.
Example C, find and replace: So my pri@@Lucy## school was Skryne National School which was
about 10 minutes from my home in Dunsany. My best friends from day one were @@Jenny##,
@@Lucy## and @@Cath## and in fact I am still very good friends with @@Lucy## to this day.
She lives in the same parish still with her husband @@Pat## and their son @@Carl##.
Example D, distracting: So my primary school was @@Oz## National School which was about
10 minutes from my home in @@Kansas##. My best friends from day one were @@Glinda##,
@@Dorothy## and @@Toto## and in fact I am still very good friends with @@Dorothy## to this
day. She lives in the same parish still with her husband @@Scarecrow## and their son
CAN ANONYMISATION BE
REVERSED?Identification through other information – jigsaw identification
For example, PEI published reports where schools/communities are named
“The ‘other information’ needed to perform re-identification could be information
available to certain organisations, to certain members of the public or that is
available to everyone because it has been published on the internet” (ICO
Anonymisation Code, pg. 18) e.g.
Identification though linked data
For example, longitudinal research results in large volume of information about an
individual
“At one end of the spectrum, pseudonymised or de-identified data may be very
valuable to researchers because of its individual-level granularity and because
pseudonymised records from different sources can be relatively easy to match.
However, this also means that there is a relatively high re-identification risk” (ICO
Anonymisation Code, pg. 36)
Identification through linking datasets
For example, where researcher has shared various datasets which could be matched
“Data quarantining - The technique of only supplying data to a recipient who is
unlikely or unable to have access to the other data needed to facilitate re-
identification. (ICO Anonymisation Code, pg. 51)
Also Deterministic modification: the same original value is always replaced by the
FINALLY, CHOOSE A SUITABLE
ACCESS OPTION
ACCESS
CONDITIONS
THAT CAN BE
APPLIED IN
COMBINATION
WITH
ANONYMISATI
ON
User
authentication
Suitable for widely
publicising data
Open access
Special
licence
Suitable for
controlling
the type of
user / use of
data
Special
condition(s)
Limited to
specified
group
Suitable for reduced
dataset (e.g. RMFs)
Controlled
on-site
AFTER SHARING: THE VALUE OF
HAVING A LONG TERM PLAN OR
POLICY
o Version control – alternative version are labelled correctly and
disseminated with label
o Someone raises an objection to their data in the archive – who will be
involved in this conversation?
o Data becomes less sensitive after a time – can anon be reversed or
reduced?
o Someone wants to use the data for something you didn’t think about
before
PART 3: QUICK START TIPS More detailed info
coming next
STARTING OUT WITH RESEARCH DATA
ANONYMISATIONSpot and highlight the direct identifiers
o Eyeball the SPSS file to pick out obvious identifiers – start by scanning the variables and
codebook
o Read the interview transcript
Assess the indirect identifiers
o Does interview participant indirectly disclose their own identity (e.g. place of residence,
place of work …)
o Does the research file indirectly disclose / harm a third party (e.g. says something negative
about someone at work)
o Does missing data disclose the identity of unique research participant? (e.g. majority of
participants responded N/A to an ethnicity question)
Take a ‘whole picture’ approach
o Run some descriptive statistics and cross tabs to see unique cases and how combinations
of variables are disclosive, and assess where data needs to be aggregated or redacted
o Scribble by hand the specific information that can be extracted from the text as you read
USUAL STEPS IN ANONYMISING
QUANTITATIVE DATA (ADAPTED FROM UKDA GUIDE)
1. Remove direct identifiers / Pseudonymisation*
2. Aggregate variables
3. Redact variables where aggregation / psuedonymisation not an option
Consider whether dropping a variable entirely would reduce risk of identification or
disclosure, and assess affect of dropping this variable on the usefulness of the
dataset. Redaction process should be noted in contextual info (e.g. “Respondents
country of birth and redacted during anonymisation”)
4. Re-assess using a combination of variables to spot any remaining disclosure risk
* Pseudonymisation differs from anonymisation “as the original provider of the
information may retain a means of identifying individuals” (Gavin, Kelly, Nic
Gabhainn and O’Callaghan 2011, pg. 20)
USUAL STEPS IN ANONYMISING
QUALITATIVE DATA
1. Remove direct identifiers / Pseudonymisation
“My name is Mary Ryan” --- “My name is @@Patricia Murphy##”
2. Aggregate / blur direct identifiers
“I grew up in Dun Laoighre” --- “I grew up in @@town in Dublin region##”
3. Consider redacting statements with increased risk of identification or
harm. Redacted text should be marked in the text – a short note can also be
inserted, e.g.
“I was upset because [text removed here – interviewee briefly describes
upsetting event]. Anyway I would rather not have that story recorded”.
4. After completing anon, re-read text as a whole (plus related texts) to
assess remaining disclosure risks
KEY POINTS FOR ANONYMISING SOCIAL
RESEARCH DATAo Think about anonymisation and sharing data from the very start to reduce your workload in the long run
o Factor in time and cost of anonymisation in your application for research funding
o Seek advice from the archives (lots online)
o As far as reasonably possible inform participant (data subject) of plans to store and share data as early as
possible
o Don’t rule out retrospective consent from participants
o Set out a procedure for anonymisation as early as possible – anonymisation should be consistent
throughout collection
o When possible, the researcher (data creator) should conduct the anonymisation or supervise
anonymisation by others
o Anonymisation requires a ‘whole picture’ approach especially where data is linked
o Good anonymisation is a balance between providing rich information that is useful in a range of contexts
with protecting the privacy of individuals – can you provide indicative categories / meaningful
pseudonyms?
o Throughout the anonymisation process keep accurate record of changes made
SOME USEFUL RESOURCES
REFERENCED IN THIS
PRESENTATION
The UK Anonymisation Network free online course gives a taster of the things you need to
consider if you want to anonymise data effectively:
http://ukanon.net/ukan-resources/course/#0.1
See also a range of resources at http://ukanon.net/
The IQDA Best Practice in Archiving Qualitative Data guide
https://content.web.nuim.ie/sites/default/files/assets/document/IQDA_Best_Practice_Han
dbook_0.pdf
See also useful tips at https://content.web.nuim.ie/iqda/deposit-data/preparing-your-
data-depositing
The Information Commissioners Office (UK) anonymisation code of practice
https://ico.org.uk/media/1061/anonymisation-code.pdf
PUBLICATIONS CITED IN THIS PRESENTATION
• Ruth Geraghty (2014) Attitudes to Qualitative Archiving in Ireland: Findings from a Consultation with the Irish Social Science Community. Studia Socjologiczne, 3. ISSN 0039−3371 available on
Maynooth University Eprints at http://eprints.maynoothuniversity.ie/6407/
• Veerle Van den Eynden, Louise Corti, Matthew Woolard, Libby Bishop and Laurence Horton (2011) Managing and Sharing Data. Essex: UK Data Archive
• Information Commissioners Office (year unknown) Anonymisation: managing data protection risk code of practice. Accessed 16th June 2016. Available at
https://ico.org.uk/media/1061/anonymisation-code.pdf
• 2nd SC6 BDE Hangout blog post written by milonjb. Posted on 6th June 201. Accessed 16th June 2016. Available at: https://www.big-data-europe.eu/recording-of-the-sc6-bde-hangout-
webinar/
• Data Protection Act 1988 (2003) available at: http://www.irishstatutebook.ie/eli/2003/act/6/enacted/en/html
• Data Protection Act 1998 available at: http://www.legislation.gov.uk/ukpga/1998/29/contents
• EU General Data Protection Regulation 2016 available at: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv:OJ.L_.2016.119.01.0001.01.ENG&toc=OJ:L:2016:119:TOC
• UK Anonymisation Network website available at: http://ukanon.net/
• Daniel Schmidutz, Lorna Ryan, Anje Muller Gjesdal and Koenraad De Smedt (30 June 2013) Report about New IPR Challenges from DASISH Work Package 6: "Legal and Ethical Issues“, Accessed
16th June 2016. Available at: http://dasish.eu/publications/projectreports/D6.1_final.pdf
• Rosalyn Moran (2016) Proposals for an Enabling Data Environment for Health and Related Research in Ireland. Dublin: Health Research Board
• Data Protection Commissioner (2007) Data Protection Guidelines on research in the Health Sector. Accessed 16th June 2016. Available at:
https://www.dataprotection.ie/documents/guidance/Health_research.pdf
• Louise Corti, Annette Day & Gill Backhouse (2000) Confidentiality and Informed Consent: Issues for Consideration in the Preservation of and Provision of Access to Qualitative Data Archives,
Accessed 16th June 2016. Available at: http://www.qualitative-forschung.de/institut/index.html
• UK Data Archive Create and Manage Data: Consent and Ethics, Accessed 16th June 2016. Available at: http://www.data-archive.ac.uk/create-manage/consent-ethics/consent?index=3
• IQDA and Tallaght West CDI Best Practice in Archiving Qualitative Data, Accessed 16th June 2016. Available at:
https://content.web.nuim.ie/sites/default/files/assets/document/IQDA_Best_Practice_Handbook_0.pdf
• A Gavin, C Kelly, S Nic Gabhainn and E O’Callaghan (2011) Key issues for consideration in the development of a data strategy: A review of the literature. Dublin: Department of Children and
Youth Affairs. Available at www.dcya.ie
Ruth Geraghty
Data Curator, CRNINI-PEI Research Initiative
Children’s Research Network for Ireland and
Northern Ireland
Email:
rgeraghty.crn@effectiveservices.org
Tel: (01)4160530
Twitter: @RuthoGeraghty
www.childrensresearchnetwork.org

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Anonymisation and Social Research

  • 1. ANONYMISATION AND SOCIAL RESEARCHRuth Geraghty Data Curator on the CRNINI-PEI Research Initiative Children’s Research Network for Ireland and Northern Ireland Anonymising Research Data workshop, University College Dublin, 22nd June 2016 www.childrensresearchnetwork.org
  • 2. THIS PRESENTATION Part 1: The role of anonymisation in social research • Why focus on anonymisation? • What legal requirements should we be aware of? • What ethical considerations should we think about? Part 2: Incorporating anonymisation into your research design • Before you start your research • Assessing sensitivity before sharing • Set a protocol or procedure for anonymisation • Choose a suitable access option Part 3: Quick start tips • General tips to get you started
  • 3. PART 1: THE ROLE OF ANONYMISATION IN SOCIAL RESEARCH Legal and ethical considerations
  • 4. WHY FOCUS ON ANONYMISATION?
  • 5. IN THE MIDST OF A CHANGE IN PRACTICE TOWARDS SHARING DATA Context of the recent past: RACcER: Re-use and Archiving of Complex Community-Based Evaluation Research (IQDA and Tallaght CDI) • 2009-2010 • 30 individuals from academic, policy and practice setting in ROI and NI AT THE TIME OF INTERVIEW ARCHIVING WAS A VERY NEW CONCEPT FOR MOST OF THE RESPONDENTS AND MANY FELT THAT THEY, “WOULDN'T EVEN BE COMFORTABLE ASKING SOMEONE TO AGREE TO ARCHIVE THE INFORMATION” BECAUSE THEY, “DON’T KNOW ENOUGH ABOUT WHAT IMPLICATIONS [ARCHIVING] WOULD HAVE FOR [PARTICIPANTS]” (Geraghty 2014, pg. 9).
  • 6. VERY RECENT DEVELOPMENTS IN IRELAND WITH POSITIVE IMPLICATIONS FOR ARCHIVING AND SHARING June 2015: Launch of Digital Repository of Ireland in as national repository for humanities and social science research data – IQDA a founder member – digital infrastructure and robust policy for sharing and making available of Irish social science data – DRI awarded Data Seal of Approval in 2015 Autumn 2015: Establishment of permanent Data Manager role in ISSDA (UCD Library) May 2016: Launch of HRB report Proposals for an Enabling Data Environment for Health and Related Research in Ireland calls for the implementation of a data access, storage, sharing and linkage (DASSL) model for health data in Ireland generated both by administrative and research sources Sep 2015 – July 2017: The Atlantic Philanthropies fund the CRNINI-PEI Research Initiative to archive legacy research data from 50+ independent evaluations conducted between 2004 – 2014 – this fund including dissemination of grants to promotes secondary analysis INDICATIVE OF A GROWING INTEREST IN ARCHIVING AND SHARING SOCIAL SCIENCE DATA AND THE NEED THEREFORE FOR PRACTICAL GUIDANCE ON HOW TO PREPARE RESEARCH DATA FOR SHARING
  • 7. THREE STEPS TO SAFELY SHARING RESEARCH WITH HUMAN SUBJECTS “RESEARCH DATA – EVEN SENSITIVE AND CONFIDENTIAL DATA – CAN BE SHARED ETHICALLY AND LEGALLY IF RESEARCHERS PAY ATTENTION, FROM THE BEGINNING OF RESEARCH, TO THREE IMPORTANT ASPECTS: • WHEN GAINING INFORMED CONSENT, INCLUDE PROVISION FOR DATA SHARING • WHERE NEEDED, PROTECT PEOPLE’S IDENTITIES BY ANONYMISING DATA • CONSIDER CONTROLLING ACCESS TO DATA” (UK DATA ARCHIVE’S MANAGING AND SHARING DATA BEST PRACTICE, pg. 23) CONSENT ANONYMITY ACCESS CONTROL
  • 8. ANONYMISATION “WE USE THE TERM ‘ANONYMISED DATA’ TO REFER TO DATA THAT DOES NOT ITSELF IDENTIFY ANY INDIVIDUAL AND THAT IS UNLIKELY TO ALLOW ANY INDIVIDUAL TO BE IDENTIFIED THROUGH ITS COMBINATION WITH OTHER DATA.” (ICO ANONYMISATION CODE OF PRACTICE, pg. 6) DIRECT: immediately identifiable information e.g. name and address, photographs INDIRECT: identifiable through combination of information e.g. linked information Anonymisation requires a ‘whole picture’ approach ANONYMITY
  • 9. BALANCE BETWEEN PROVIDING RICH INFORMATION THAT IS USEFUL IN A RANGE OF CONTEXTS WITH PROTECTING THE PRIVACY OF INDIVIDUALS Name: Enda Kenny Date of birth: 24 April 1951 Education: B.A. Education, University College Galway Place of birth: Derrywash, Islandeady, County Mayo Current employment: An Taoiseach and Minister for Defence Family status: Married to Fionnuala O'Kelly, three children Name: @@Pat Keegan## Age group: 50 - 65 Highest education: Degree Place of birth: West region of Ireland Current employment: Politician Family status: Married, <5 children Anonymisat ion process Combination of pseudonym, aggregation and redaction results in data that indicates characteristics of participant but is not specific enough to disclose identity
  • 10. Variable Case 1 Case 2 Name Viacheslav Fesenko Chris Fournier DOB 18 April 1981 04 June 1987 Highest Ed. B.Ss. Sports Science B.Sc. Sports Rehabilitation Place of birth Kiev, Ukraine Lyon, France Current residence Mullingar, Co. Westmeath Celbridge, Co. Kildare Sport Professional basketball player Professional basketball player Months playing with Irish team 18 36 Variable Case 1 Case 2 Name Case 01 Case 02 Age group 29 - 35 29 - 35 Highest Ed. Degree (sport and fitness) Degree (sport and fitness) Place of birth Europe (non Irish) Europe (non Irish) Current residence Leinster Leinster Sport Professional basketball player Professional basketball player Months playing with Irish team 18 36 Anonymisa tion processYou decide which information is really key to your database and which information you can afford to alter or remove In this case the really key information that is left intact is their specific sport and their length of time with Irish team. Useful info such as where they moved to Ireland from and where exactly they are living and working is retained using broader categories.
  • 12. EUROPEAN AND DOMESTIC ACTS WITH RELEVANCE TO STORING AND SHARING RESEARCH DATAEUROPEAN General Data Protection Regulation (Regulation (EU) 2016/679 and Directive (EU) 2016/680) Replaces the European Data Protection Directive (95/46/EC) Stronger regulation to take into account vast technological changes of the last 20 years – all member states have to transpose it into their national law by 6 May 2018 “It has also turned out to be fairly research-friendly as the most important special provisions from the Directive are continued, clarified and strengthened. Amongst other this applies to the possibility to process personal data based on other grounds than consent.” (Big Data Europe blog, 6th June 2016). DOMESTIC REPUBLIC OF IRELAND Data Protection Act 1988 and Amendment Act 2003 UNITED KINGDOM Data Protection Act 1998
  • 13. ‘PERSONAL DATA’ LEGAL DEFINITION UK Data Protection Act 1998 “personal data” means data which relate to a living individual who can be identified — (a) from those data, or (b) from those data and other information which is in the possession of, or is likely to come into the possession of, the data controller, and includes any expression of opinion about the individual and any indication of the intentions of the data controller or any other person in respect of the individual ROI Data Protection Act 1988 (2003) “personal data” means data relating to a living individual who can be identified either from the data or from the data in conjunction with other information in the
  • 14. (26) The principles of data protection should therefore not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable. This Regulation does not therefore concern the processing of such anonymous information, including for statistical or research purposes. ANONYMISATION IN THE CONTEXT OF NEW EU GENERAL DATA PROTECTION REGULATION
  • 15. ANONYMISATION IN THE CONTEXT OF ROI DATA PROTECTION ACT 1988 (AMENDMENT ACT 2003) In the context of anonymisation: “IRREVOCABLE ANONYMISATION OF PERSONAL DATA PUTS IT OUTSIDE DATA PROTECTION REQUIREMENTS AS THE DATA CAN NO LONGER BE LINKED TO AN INDIVIDUAL AND THEREFORE CANNOT BE CONSIDERED TO BE PERSONAL DATA.” (DPC DATA PROTECTION GUIDELINES ON RESEARCH IN THE HEALTH SECTOR 2007, PG. 9) Existing provision for archiving data for research purposes: Section 2. Collection, processing, keeping, use and disclosure of personal data (c) the data— (iv) shall not be kept for longer than is necessary for that purpose or those purposes (5) (a) Paragraph (c) (iv) of the said subsection (1) does not apply to personal data kept for historical, statistical or research purposes if the data are not used in such a was that damage or distress is, or is likely to be, caused to any data subject.
  • 16. ANONYMISATION IN THE CONTEXT OF UK DATA PROTECTION ACT 1998 DATA PROTECTION LAW DOES NOT APPLY TO DATA RENDERED ANONYMOUS IN SUCH A WAY THAT THE DATA SUBJECT IS NO LONGER IDENTIFIABLE (INFORMATION COMMISSIONERS OFFICE ANONYMISATION CODE OF PRACTICE, pg. 6) Part iv of the Data Protection Act 1998 on exemptions UK Anonymisation Network (UKAN) set up in 2012 as a means of establishing best practice in anonymisation with funding by the Information Commissioner’s Office (ICO) during for its first two years. “Organisations that want or need to share, disseminate or publish their data for secondary use are obliged under the DPA (1998), unless exempt, to transform the data in such a way as to render it anonymous and therefore no longer personal” (UK Anonymisation Network website)
  • 17. INDEPENDENT AUTHORITIES FOR DATA PRIVACYRepublic of Ireland Office of the Data Protection Commissioner https://www.dataprotection.ie/ United Kingdom Information Commissioners Office https://ico.org.uk Anonymisation code of practice: https://ico.org.uk/media/1061/anonymisation-code.pdf
  • 19. CHANGING LANDSCAPE OF ETHICS IN TERMS OF SHARING DATAIn the past clause to destroy data after a certain period as an ethical commitment to protect individuals from harm Recent developments that are challenging existing codes for research ethics o Research with a central commitment to secondary analysis e.g. Growing Up in Ireland the National Longitudinal Study of Children o ‘New data’ types (objective rather than self-reported) e.g. harvested social media data, research data linked with other information sources o New approaches to analysis e.g. big data analysis, meta analysis and data mining o Research grants that promote re-using data that already exists (‘value for money’ discourse) o Research grants that promote archiving and sharing as an aspect of data management plan (‘good science’ discourse) o Open data and data democracy o Digital dissemination platforms e.g. public data archives, web-access
  • 20. INTERACTION BETWEEN ETHICS AND LAW IN REGARD TO DATA PROTECTION Ethics: moral judgement, what ought to be done Law: codified rule, what must be done Data protection “RESEARCH ETHICS COMMITTEES (RECS) OPERATE AT THE INTERFACE BETWEEN THE LEGAL SYSTEM AND ETHICAL FRAMEWORKS… SIGNING OFF ON RESEARCH PROPOSALS BY A RESEARCH ETHICS COMMITTEE MAY BE LINKED TO INDEMNIFYING THE RESEARCHER AGAINST ANY LEGAL ACTION ARISING FROM THE RESEARCH." (DASISH REPORT ABOUT NEW IPR CHALLENGES 2013, pg. 10)Key aspects of ethics • ‘Do no harm’ principle • Professional integrity, reputation of the discipline • Researchers position becomes relevant – pluralism is a challenge
  • 21. ETHICS AND ANONYMISATION Ethical assessment tends to be more complex and nuanced than rule of law For example: Case of the request for deceased mother’s transcript ROI Data Protection Act 1988 (2003) definition of “personal data” means data relating to a living individual “WHERE PATIENT DATA IS ANONYMISED, THERE IS NO NEED FROM A DATA PROTECTION PERSPECTIVE TO SEEK THE CONSENT OF PATIENTS FOR THE USE OF THE DATA FOR RESEARCH AND CLINICAL AUDIT PURPOSES. THERE MAY, OF COURSE, BE ETHICAL CONSIDERATIONS IN SOME CASES BUT THESE ARE OUTSIDE THE SCOPE OF THESE GUIDELINES” (DPC DATA PROTECTION GUIDELINES ON RESEARCH IN THE HEALTH SECTOR 2007, PG. 9)
  • 22. CASE STUDY: CRNINI-PEI RESEARCH INITIATIVE AT THE CHILDREN’S RESEARCH NETWORK FOR IRELAND AND NORTHERN IRELAND o 50+ independent evaluations funded by The Atlantic Philanthropies between 2008 - 2014 o Legacy data of great value - public good principle o Retrospective archiving - Biggest problem – was not planned for Legal requirement where consent is missing but anonymisation carried out… Resulting in the following flow chart… CONSEN T
  • 23. FLOW CHART TO ASSESS POSSIBILITY OF ARCHIVING PEI DATA ANONYMIT Y ACCESS CONTRO L CONSEN T
  • 24. PART 2: INCORPORATING ANONYMISATION INTO YOUR RESEARCH DESIGN General principles
  • 25. BEFORE YOU START YOUR RESEARCH: CONSENT
  • 26. GAINING ETHICAL APPROVAL AND ROLE OF ANONYMISATION Make your case to ethics review board for approval to do both: (1) consent to participate in the research (2) consent to archive/share their (anonymised) data following the research Retrospective consent – it is possible! “MOST [RESEARCH PARTICIPANTS] DO BELIEVE THAT RESEARCH IS FOR THE PUBLIC GOOD, AND THAT THEIR CONTRIBUTION WILL BE USED IN SOME WAY TO CREATE A BETTER INFORMED SOCIETY, AND EVEN GO SOME WAY TOWARDS IMPLEMENTING POLICY CHANGES. (Corti, Day & Backhouse 2000) Retrospective ethical approval – also possible as landscape changes, sensitivities diminish over time and apparent value of data increases
  • 27. INFORMED CONSENT EXAMPLEAdapted from samples available from the UK Data Archive at http://www.data-archive.ac.uk/create-manage/consent-ethics/consent?index=3 I agree to take part in the project. Taking part in the project will include being Yes No interviewed and recorded (audio or video) I understand that my personal details such as phone number and address will Yes No not be revealed to people outside the project I agree for the data I provide to be archived at [archive name] Yes No I understand that other authenticated researchers will have access to this data only Yes No if they agree to preserve the confidentiality of the information as requested in this formMake sure the agreement that you come to with the archive on access replicates what you have committed to in the consent form
  • 29. RISK ASSESSMENT About managing risk rather than removal of all risk: “The risk of disclosure is not zero (it should be negligible)” (UKAN website) “ The [UK Data Protection Act 1998] does not require anonymisation to be completely risk free – you must be able to mitigate the risk of identification until it is remote.” (ICO Anonymisation Code of Practice, pg. 6 The value of discussing a decision with your team The value of consulting the experts on archiving and sharing No hard and fast rules – but some tools…
  • 30. TOOL A: IQDA ASSESSING SENSITIVITY LEVEL OF THE DATA Two parameters o identification o harm “IS THERE A RISK THAT THE TEXT WILL ALLOW THE RESEARCH PARTICIPANT TO BE IDENTIFIED? IS THERE A RISK THAT THE TEXT WILL BE HARMFUL TO THE RESEARCH PARTICIPANT? FOR EXAMPLE, MIGHT THE TEXT EXPOSE THE PARTICIPANT TO RIDICULE OR HAVE OTHER ADVERSE CONSEQUENCES FOR THEM? BY EXAMINING THESE TWO PARAMETERS IT IS POSSIBLE TO MAKE AN OVERALL ASSESSMENT OF THE RISK LEVEL OF THE DATA” (IQDA AND TALLAGHT CDI BEST PRACTICE IN ARCHIVING QUALITATIVE DATA PG. 6) Risk of Identification Risk of Harm Sensitivity Level Little Low Low Some Low Medium Any High High
  • 31. Step 1. Assess the data in front of you whether it is aggregate data or data with individuals what the key variables are what is likely to affect the risk level of your data such as sensitivity of your data disclosiveness of your data (many variables for example) who has access to the data Step 2. Scenario analysis other relevant external data sources potential intruders the likelihood of an attempt of attack on dataset This information will help you establish relevant plausible scenarios for your data situation. When you undertake a scenario analysis, you are essentially considering the how, who and why of a potential breach 3. Testing the scenarios TOOL B: UKAN ASSESSMENT OF DISCLOSURE RISK (FROM SECTION 6 OF ONLINE TRAINING*) * http://ukanon.net/ukan-resources/course/#0.1
  • 32. SET A PROTOCOL FOR ANONYMISATION (ESPECIALLY FOR TEAM WORK)
  • 33. ANONYMISATION IS ALTERING DATA Keep track of changes during the process - The ‘changes file’ Note changes in the contextual materials Team work o Protocol very important o Consistency across the way data is altered o Check in regularly
  • 34. WHEN BAD ANONYMISATION HAPPENS TO GOOD DATA…Original: So my primary school was Skryne National School which was about 10 minutes from my home in Dunsany. My best friends from day one were Karen, Mary and Sarah and in fact I am still very good friends with Mary to this day. She lives in the same parish still with her husband John and their son Cian. Example A, too light: So my primary school was [name] National School which was about 10 minutes from my home in Dunsany. My best friends from day one were Karen, Mary and Sarah and in fact I am still very good friends with Mary to this day. She lives in the same parish still with her husband John and their son Cian. Example B, too heavy: So my primary school was X which was about X minutes from my home in X. My best friends from day one were X, X and X and in fact I am still very good friends with X to this day. She lives in the same parish still with her husband X and their X X. Example C, find and replace: So my pri@@Lucy## school was Skryne National School which was about 10 minutes from my home in Dunsany. My best friends from day one were @@Jenny##, @@Lucy## and @@Cath## and in fact I am still very good friends with @@Lucy## to this day. She lives in the same parish still with her husband @@Pat## and their son @@Carl##. Example D, distracting: So my primary school was @@Oz## National School which was about 10 minutes from my home in @@Kansas##. My best friends from day one were @@Glinda##, @@Dorothy## and @@Toto## and in fact I am still very good friends with @@Dorothy## to this day. She lives in the same parish still with her husband @@Scarecrow## and their son
  • 35. CAN ANONYMISATION BE REVERSED?Identification through other information – jigsaw identification For example, PEI published reports where schools/communities are named “The ‘other information’ needed to perform re-identification could be information available to certain organisations, to certain members of the public or that is available to everyone because it has been published on the internet” (ICO Anonymisation Code, pg. 18) e.g. Identification though linked data For example, longitudinal research results in large volume of information about an individual “At one end of the spectrum, pseudonymised or de-identified data may be very valuable to researchers because of its individual-level granularity and because pseudonymised records from different sources can be relatively easy to match. However, this also means that there is a relatively high re-identification risk” (ICO Anonymisation Code, pg. 36) Identification through linking datasets For example, where researcher has shared various datasets which could be matched “Data quarantining - The technique of only supplying data to a recipient who is unlikely or unable to have access to the other data needed to facilitate re- identification. (ICO Anonymisation Code, pg. 51) Also Deterministic modification: the same original value is always replaced by the
  • 36. FINALLY, CHOOSE A SUITABLE ACCESS OPTION
  • 37. ACCESS CONDITIONS THAT CAN BE APPLIED IN COMBINATION WITH ANONYMISATI ON User authentication Suitable for widely publicising data Open access Special licence Suitable for controlling the type of user / use of data Special condition(s) Limited to specified group Suitable for reduced dataset (e.g. RMFs) Controlled on-site
  • 38. AFTER SHARING: THE VALUE OF HAVING A LONG TERM PLAN OR POLICY o Version control – alternative version are labelled correctly and disseminated with label o Someone raises an objection to their data in the archive – who will be involved in this conversation? o Data becomes less sensitive after a time – can anon be reversed or reduced? o Someone wants to use the data for something you didn’t think about before
  • 39. PART 3: QUICK START TIPS More detailed info coming next
  • 40. STARTING OUT WITH RESEARCH DATA ANONYMISATIONSpot and highlight the direct identifiers o Eyeball the SPSS file to pick out obvious identifiers – start by scanning the variables and codebook o Read the interview transcript Assess the indirect identifiers o Does interview participant indirectly disclose their own identity (e.g. place of residence, place of work …) o Does the research file indirectly disclose / harm a third party (e.g. says something negative about someone at work) o Does missing data disclose the identity of unique research participant? (e.g. majority of participants responded N/A to an ethnicity question) Take a ‘whole picture’ approach o Run some descriptive statistics and cross tabs to see unique cases and how combinations of variables are disclosive, and assess where data needs to be aggregated or redacted o Scribble by hand the specific information that can be extracted from the text as you read
  • 41. USUAL STEPS IN ANONYMISING QUANTITATIVE DATA (ADAPTED FROM UKDA GUIDE) 1. Remove direct identifiers / Pseudonymisation* 2. Aggregate variables 3. Redact variables where aggregation / psuedonymisation not an option Consider whether dropping a variable entirely would reduce risk of identification or disclosure, and assess affect of dropping this variable on the usefulness of the dataset. Redaction process should be noted in contextual info (e.g. “Respondents country of birth and redacted during anonymisation”) 4. Re-assess using a combination of variables to spot any remaining disclosure risk * Pseudonymisation differs from anonymisation “as the original provider of the information may retain a means of identifying individuals” (Gavin, Kelly, Nic Gabhainn and O’Callaghan 2011, pg. 20)
  • 42. USUAL STEPS IN ANONYMISING QUALITATIVE DATA 1. Remove direct identifiers / Pseudonymisation “My name is Mary Ryan” --- “My name is @@Patricia Murphy##” 2. Aggregate / blur direct identifiers “I grew up in Dun Laoighre” --- “I grew up in @@town in Dublin region##” 3. Consider redacting statements with increased risk of identification or harm. Redacted text should be marked in the text – a short note can also be inserted, e.g. “I was upset because [text removed here – interviewee briefly describes upsetting event]. Anyway I would rather not have that story recorded”. 4. After completing anon, re-read text as a whole (plus related texts) to assess remaining disclosure risks
  • 43. KEY POINTS FOR ANONYMISING SOCIAL RESEARCH DATAo Think about anonymisation and sharing data from the very start to reduce your workload in the long run o Factor in time and cost of anonymisation in your application for research funding o Seek advice from the archives (lots online) o As far as reasonably possible inform participant (data subject) of plans to store and share data as early as possible o Don’t rule out retrospective consent from participants o Set out a procedure for anonymisation as early as possible – anonymisation should be consistent throughout collection o When possible, the researcher (data creator) should conduct the anonymisation or supervise anonymisation by others o Anonymisation requires a ‘whole picture’ approach especially where data is linked o Good anonymisation is a balance between providing rich information that is useful in a range of contexts with protecting the privacy of individuals – can you provide indicative categories / meaningful pseudonyms? o Throughout the anonymisation process keep accurate record of changes made
  • 44. SOME USEFUL RESOURCES REFERENCED IN THIS PRESENTATION The UK Anonymisation Network free online course gives a taster of the things you need to consider if you want to anonymise data effectively: http://ukanon.net/ukan-resources/course/#0.1 See also a range of resources at http://ukanon.net/ The IQDA Best Practice in Archiving Qualitative Data guide https://content.web.nuim.ie/sites/default/files/assets/document/IQDA_Best_Practice_Han dbook_0.pdf See also useful tips at https://content.web.nuim.ie/iqda/deposit-data/preparing-your- data-depositing The Information Commissioners Office (UK) anonymisation code of practice https://ico.org.uk/media/1061/anonymisation-code.pdf
  • 45. PUBLICATIONS CITED IN THIS PRESENTATION • Ruth Geraghty (2014) Attitudes to Qualitative Archiving in Ireland: Findings from a Consultation with the Irish Social Science Community. Studia Socjologiczne, 3. ISSN 0039−3371 available on Maynooth University Eprints at http://eprints.maynoothuniversity.ie/6407/ • Veerle Van den Eynden, Louise Corti, Matthew Woolard, Libby Bishop and Laurence Horton (2011) Managing and Sharing Data. Essex: UK Data Archive • Information Commissioners Office (year unknown) Anonymisation: managing data protection risk code of practice. Accessed 16th June 2016. Available at https://ico.org.uk/media/1061/anonymisation-code.pdf • 2nd SC6 BDE Hangout blog post written by milonjb. Posted on 6th June 201. Accessed 16th June 2016. Available at: https://www.big-data-europe.eu/recording-of-the-sc6-bde-hangout- webinar/ • Data Protection Act 1988 (2003) available at: http://www.irishstatutebook.ie/eli/2003/act/6/enacted/en/html • Data Protection Act 1998 available at: http://www.legislation.gov.uk/ukpga/1998/29/contents • EU General Data Protection Regulation 2016 available at: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv:OJ.L_.2016.119.01.0001.01.ENG&toc=OJ:L:2016:119:TOC • UK Anonymisation Network website available at: http://ukanon.net/ • Daniel Schmidutz, Lorna Ryan, Anje Muller Gjesdal and Koenraad De Smedt (30 June 2013) Report about New IPR Challenges from DASISH Work Package 6: "Legal and Ethical Issues“, Accessed 16th June 2016. Available at: http://dasish.eu/publications/projectreports/D6.1_final.pdf • Rosalyn Moran (2016) Proposals for an Enabling Data Environment for Health and Related Research in Ireland. Dublin: Health Research Board • Data Protection Commissioner (2007) Data Protection Guidelines on research in the Health Sector. Accessed 16th June 2016. Available at: https://www.dataprotection.ie/documents/guidance/Health_research.pdf • Louise Corti, Annette Day & Gill Backhouse (2000) Confidentiality and Informed Consent: Issues for Consideration in the Preservation of and Provision of Access to Qualitative Data Archives, Accessed 16th June 2016. Available at: http://www.qualitative-forschung.de/institut/index.html • UK Data Archive Create and Manage Data: Consent and Ethics, Accessed 16th June 2016. Available at: http://www.data-archive.ac.uk/create-manage/consent-ethics/consent?index=3 • IQDA and Tallaght West CDI Best Practice in Archiving Qualitative Data, Accessed 16th June 2016. Available at: https://content.web.nuim.ie/sites/default/files/assets/document/IQDA_Best_Practice_Handbook_0.pdf • A Gavin, C Kelly, S Nic Gabhainn and E O’Callaghan (2011) Key issues for consideration in the development of a data strategy: A review of the literature. Dublin: Department of Children and Youth Affairs. Available at www.dcya.ie
  • 46. Ruth Geraghty Data Curator, CRNINI-PEI Research Initiative Children’s Research Network for Ireland and Northern Ireland Email: rgeraghty.crn@effectiveservices.org Tel: (01)4160530 Twitter: @RuthoGeraghty www.childrensresearchnetwork.org

Editor's Notes

  1. Article 5 principles relating to personal data processing (b) collected for specified, explicit and legitimate purposed and not further processed in a way incompatible with those purposes; further processing of personal data for archiving purposes in the public interest or scientific, statistical or historical purposes shall in accordance with Article 89 not be considered incompatible with the initial purposes