The document discusses the balance between disclosure risks and data utility in the context of microdata management, specifically focusing on the Australian Early Development Census (AEDC) conducted by the Social Research Centre. It outlines traditional and alternative approaches to risk management, emphasizing the need for shared responsibility between data custodians and researchers while ensuring data protection and professional conduct. Additionally, it highlights the potential for enhanced data utility through anonymization techniques while addressing concerns about misuse and the importance of proper training and access protocols.

1. Risk management and the release
of microdata:
Sonia Whiteley & Eric Skuja
The Social Research Centre
IAOS Conference on Official Statistics,
Da Nang Viet Nam, 8-10 October 2014
balancing disclosure risks and data utility

2. About the Social Research Centre (1)
 We are a private, for profit company owned by ANU
Enterprise, a subsidiary of the Australian National
University and co-founder of the Australian Centre for
Applied Social Research Methods (AusCen).
 Our resources include 60 professional staff, 125 station
call centre, a panel of 250 interviewing staff and qualitative
interviewing facilities.
 Typical services provided include survey design and
execution (including sampling, questionnaire design,
survey administration and interviewer training), qualitative
research, survey data management, statistical consulting
and analytical and interpretative reporting.

3. About the Social Research Centre (2)
We conduct a number of large scale surveys that contribute
to the annual Report on Government Services (ROGS) which
provides information on the equity, effectiveness and
efficiency of government services in Australia including the:
 National Survey of Community Satisfaction with Policing
(NSCSP)
 Student Outcomes Survey (SOS)
 Australian Early Development Census (AEDC).

4. About the AEDC (1)
 The Australian Early Development Census (AEDC) is
conducted every three years for every Australian child
currently enrolled in their first year of full-time school.
 Approximately 100 checklist questions cover the five
theoretical domains of early childhood development
including:
− Physical health and well being
− Social competence
− Emotional maturity
− Language and cognitive skills (school-based), and
− Communication skills and general knowledge.

5. About the AEDC (2)
 The AEDC was conducted for the first time in 2009 with a
second collection in 2012. Preparations are already
underway for the 2015 AEDC collection.
 Approximately 290,000 checklists were completed for
each AEDC which equates to more than 96 per cent of in-
scope children.
 There are three school systems in Australia, Government,
Catholic and Independent and all actively participate in the
AEDC.

6. Our role in the AEDC
Our involvement in the AEDC is ongoing and includes:
 Collecting checklist data from teachers via a secure, online
system
 Developing and maintaining a government website
containing
− AEDC resources http://www.aedc.gov.au/ and
− AEDC macrodata and maps http://www.aedc.gov.au/data
 Managing and disseminating the AEDC data collections.

7. Traditional risk management of microdata
 A ‘worst case’ scenario approach requires the data
custodians to be responsible for identifying and mitigating
all potential risks
 This model assumes that data users are
 Unprofessional
 Cannot be trusted
 Do not have the required skills or training, and
 (In extreme cases) Intend to maliciously misuse the data
 Data must be protected from the users and the utility of the
unit record data for research is a lower priority
 The product of this approach is typically a confidentailised
unit record file and the confidentialisation is regarded as
the primary safeguard

8. Initial approach to AEDC data management
 The AEDC Data Protocol and the AEDC Linkage Policy
provide the research community with guidance regarding
the appropriate uses of the data.
 Two confidentialised unit record files (CURFs) were
produced:
1. The Research CURF and
2. The Geography CURF
 The files were split due to the
− large number of demographic variables
− fine level of geographic information that was available, and
− concerns about the re identification and disclosure of
children, classes and schools

9. Perturbation issues
 Both files were perturbed by an external agency based on
a ‘worst case scenario’ view of risk
 The perturbation rules were undisclosed to prevent
reverse engineering
 Key variables of relevance to early childhood education
researchers were changed significantly
− In particular, gender of the child was altered substantially in
some geographic areas
 Government agencies started to use the CURFs because
they were smaller and more accessible files which led to
discrepancies between the official results and the agency
results

10. Alternative approaches to risk management
 The responsibility for appropriately using and reporting
microdata is shared between the data custodian and the
research community
 It is assumed that researchers will observe their
professional codes of conduct and do not intend to misuse
the data
 The main risk focus is on ensuring that researchers
appropriately handle, store and publish the data. This
includes:
Confirming a genuine research aim
Restriction of data access to authorised users
Ensuring the microdata is anonymised, and
Undertaking a risk assessment of files prior to release

11. Formal risk assessments
 Government departments are still extremely risk averse,
especially when it comes to information about very young
children. Two simple risk assessments accompany each
microdata file. Both act as topics for negotiation and
support rather than obstruction:
1. The proportion of unique records in the dataset is assessed
mainly to discourage unauthorised data linkage projects
being undertaken by other government agencies.
2. The proportion of cells in two-way tables with three or fewer
children is calculated to foreshadow potential problems
when researchers publish the data.

12. High risk data
Built Environment measures
 Child Friendly Neighbourhoods: A composite index based on the
following built environment measures requires the children’s X-Y
coordinates:
− Child health resources Proximity of childcare facilities.
− Parks and greenness Proximity of neighbourhood parks.
− Residential density Number of residential dwellings.
− Home environment Type of residence. Size of backyard.
− Traffic exposure Road network classifications
− Crime Crime and child related offenses
− Land use mix Evenness of different land uses
− Public transport Accessibility of bus and rail stops
− Street connectivity Number of 3-way or more intersections

13. Managing high risk data
Points dispersed within a mesh block

14. Implications of using anonymised microdata
 Improves data utility but does not necessarily present higher
levels of disclosure risk than a CURF.
 Ensures that there is ‘one version of the truth’ and that outputs
produced by researchers will be consistent all data users.
 Ensures that access requests for any unit record file follow the
same formal, detailed assessment, management and close-out
procedures.
 Concerns about unintentional misuse of microdata need to be
clearly communicated to the research community.
 Provides an avenue for offering training and support are a
condition of data release where a potential data user may not
have the skills or experience
 Creates a platform where all genuine data access requests can
be accommodated through a combination of engaged
negotiation regarding the required data elements and by offering
supported (and supportive) access modalities.