Integrated Data Infrastructure (IDI)
Project manager – Guido Stark
Linking data across government
How Statistics New Zealand maintains privacy
in large scale data integration projects
June 2012
2008 LEED – MSD Benefit data
History
Person to Business
Link
2004 Student Loans (SL)
Student Loans
IR
MSD (StudyLink)
SLAM
EMS
Self-employed
IR
Education
Tertiary
MoE
2009 Employment Outcomes of Tertiary Education (EOTE)
2005 Linked Employer-Employee Data (LEED)
Person to Business
Link
EMS
Self-employed
IR
Person to Business
Link
EMS
Self-employed
IR
2007 Student Loans and Allowances (SLA)
Student Loans
& Allowances
IR
MSD (StudyLink)
SLAM
2007 Prototype
Longitudinal
Business Database
Benefits
MSD (BDD)
Education
Tertiary
MoE
2010 EOTE + secondary education data
Education
Secondary
MoE
Person to Business
Link
EMS
Self-employed
IR
Education
Tertiary
MoE
Education
Tertiary
MoE
2011 LEED – Household Labour Force Survey (HLFS)
LBD
Person to Business
Link
EMS
Self-employed
IR
HLFS
Stats NZ
2
Statistics 2020 – Te Kāpehu Whetū
Achieving the statistical system of the future
Lead the Official Statistics System
•
Responding to customer needs
Obtain more value from official statistics
Transform the way we deliver statistics
•
•
Maximise the use of administrative data
Increase use of data integration
Create a responsive, customer-focused, influential, and sustainable
organisation
Data integration raises real and perceived privacy risks
3
Integrated Data Infrastructure (IDI)
Project manager – Guido Stark
4
Data Integration Policy
Four key principles
1. The public benefits of integration
outweigh both:
• privacy concerns about use, and
• risks to the integrity of the
Official Statistics System or
other activities of government
2. The integrated data will only be
used for statistical or research
purposes
3. The data integration will be
conducted in an open and
transparent manner
4. Data will not be integrated
where an explicit commitment has
been given to respondents that
would preclude such action.
5
The triangle
Privacy
Safe Data
Security
Confidentiality
6
IDI security
Secure workspaces
Limited access
Access control
Regular audits of access and use
Output control
7
IDI confidentiality
Encryption / transformation
IDI rules
•
•
•
Aggregating
Rounding
Suppression
Output checking
Statistical vs operational
8
IDI privacy
Privacy Impact Assessment
•
www.stats.govt.nz/IDI
Risks
•
•
•
Information used in a way that is detrimental to their personal circumstances
Information might be released that identifies them and aspects of their personal
circumstances
Unrelated information might be collected about them in an ever-growing
database for non-specific purposes (i.e. ‘Big Brother’).
Benefits
•
•
•
Potential for new official statistics
Provides an evidence base for research, evaluation, and policy formulation
Meets Statistics NZ’s strategic priorities
Data Integration Policy
The Privacy Act 1993
Principle 1: Purpose of collection of personal information
Principle 2: Source of personal information
Principle 12: Unique identifiers
The Statistics Act 1975
Section 3: Official statistics and coordination
Section 15: Independence of the Government Statistician
Section 37: Security of Information
Section 21: Declaration of secrecy
10
Information flows
Restricted access in
accordance with
Statistics NZ’s
Security Framework
Load source
data
Source data
Unique
identifiers are
encrypted
Clean source
data
Create link
tables
Names, addresses, date
of birth removed
Access for bona-fide
statistical purposes
to required data
sources only
Clean source
data
Create core
tables
SNZ derived unique
identifier available
Outputs
Confidentiality checks
Linking
Core tables
The triangle
Privacy
Safe projects
Safe researchers
Safe Data
Security
Safe access
Confidentiality
Safe output
12
Use of the IDI
Developing regular measures of immigrant outcomes
Developing tertiary education outcome indicators
How successful is NZ in retaining qualifications
Intellectual property and productivity
Mapping post-compulsory school pathways and outcomes
The impact of immigration on the labour market
The influence of education on outcomes
What is the impact of gaining qualifications for beneficiaries
Who doesn’t participate in tertiary education
Benefit to work transitions
The effect of wage subsidies on individual and firm employment
Access
Making the most of the IDI
Secondment
Statistics NZ datalab
•
•
•
•
•
•
Legal compliance
Bona fide research
Non-regulatory
Proven researcher
Confidentiality
Suitable data source
Integrated Data Infrastructure (IDI)
?
?
?
?
Migration data
DoL
Education
secondary &
tertiary
LEED
LBD
MoE
Student Loans
& Allowances
Central Linking
Concordance
Inland Revenue
MSD (StudyLink)
SLAM
EMS
Self-employed
(CLC)
Business data
Person to
business
link
Inland Revenue
Benefits
HLFS / NZIS
Survey
MSD (BDD)
Outputs
Relevant releases
Dynamic datasets
Cutting edge cubes
Powerful research
15
Questions?
IDI Project manager – Guido Stark
guido.stark@stats.govt.nz
June 2012