This presentation was shared by John Spencer at the June 2016 MEASURE Evaluation GIS Working Group Meeting.

1. Reality check
John Spencer
MEASURE Evaluation
2016 MEASURE Evaluation GIS Working Group Meeting

2. When you see a rainbow, there are likely
clouds somewhere near-by

3. Privacy/Confidentiality
• Issues around individual level data are pretty well
understood.
• Spatial data have unique challenges regarding
privacy and confidentiality
• What policy documents exist? (USG, National
Governments, Organizations)
• What trainings exist?

4. k-anonymity
• Given person-specific field-structured data,
produce a release of the data with scientific
guarantees that the individuals who are the
subjects of the data cannot be re-identified while
the data remain practically useful.
• Latanya Sweeney, k-anonymity:a model for protecting
privacy; International Journal on Uncertainty, Fuzziness
and Knowledge-based Systems, 10 (5), 2002; 557-570

5. k-anonymity
• Data can be suppressed or generalized to achieve
suitable k-anonymity
• Still some vulnerability to deductive disclosure
• Alternatives:
• L-diversity
• T-closeness
• Differential privacy
Always a trade-off between data
integrity and privacy

6. Privacy/Confidentiality
• Action: Systematic review of methods and
techniques

7. Stigma
Data collected on certain populations may put groups
at legal or physical risk.
Once again spatial data has unique issues
• In addition to methods and approaches described
there are other considerations
• Groups of individuals at risk
• Harder to conceal concentrations of groups

8. Stigma
A Framework for Ethical Engagement with Key
Populations in PEPFAR Programs
Breyer et al
http://www.pepfar.gov/sab/210110.htm
Improving access to services for some
populations brings some risk

9. Stigma
Action: Policies/guidance on spatial display of data on
key populations.

10. Data Ownership
Who “owns” the data?
In other words, who has responsibility for:
• Access to data
• Policies around acceptable use
• Data updating
• Maintenance of hardware
• Funding

11. Data Ownership
Co-ownership?
Strengths and limitations of “co-ownership”
• Funding
• How to handle conflicts around data quality or
policies?
• National laws on data ownership
Examples of complications around sharing of data.

12. Data Ownership
Action: Clear policies around data ownership and
definition of term.
Is there a better concept than “owner”?

13. Data security
How is the data protected from unauthorized access?
DATIM
• Robust security procedures
National systems
• ?

14. Data security
National security considerations
Some countries may have restrictions on release of
information for “national security” reasons

16. Positives
• Issues are not unique to PEPFAR or spatial data
• Coincides with growth in techniques around data in
other areas
• Growth in ICT

17. Drones
Advanced data
collection
approaches
Machine learning

18. Internet of things
(sensors)
World that’s
becoming more
connected

20. What did we miss?
List issues not covered
Resources
Contact information

21. MEASURE Evaluation is funded by the U.S. Agency
for International Development (USAID) under terms
of Cooperative Agreement AID-OAA-L-14-00004 and
implemented by the Carolina Population Center, University
of North Carolina at Chapel Hill in partnership with ICF
International, John Snow, Inc., Management Sciences for
Health, Palladium Group, and Tulane University. The views
expressed in this presentation do not necessarily reflect
the views of USAID or the United States government.
www.measureevaluation.org