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Reality Check

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

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Reality Check

  1. 1. Reality check John Spencer MEASURE Evaluation 2016 MEASURE Evaluation GIS Working Group Meeting
  2. 2. When you see a rainbow, there are likely clouds somewhere near-by
  3. 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. 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. 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. 6. Privacy/Confidentiality • Action: Systematic review of methods and techniques
  7. 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. 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. 9. Stigma Action: Policies/guidance on spatial display of data on key populations.
  10. 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. 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. 12. Data Ownership Action: Clear policies around data ownership and definition of term. Is there a better concept than “owner”?
  13. 13. Data security How is the data protected from unauthorized access? DATIM • Robust security procedures National systems • ?
  14. 14. Data security National security considerations Some countries may have restrictions on release of information for “national security” reasons
  15. 15. Positives • Issues are not unique to PEPFAR or spatial data • Coincides with growth in techniques around data in other areas • Growth in ICT
  16. 16. Drones Advanced data collection approaches Machine learning
  17. 17. Internet of things (sensors) World that’s becoming more connected
  18. 18. What did we miss? List issues not covered Resources Contact information
  19. 19. 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

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