1. Census Data for Transportation Planning—Some Thoughts
George Duncan
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2. Three Themes Confidentiality Disclosure limitation methods
Multiple sources of Information
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3. Confidentiality Identified by Jay Waite of Census
More restrictions? Concern of George Schoener of DOT
Repeated theme throughout conference
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4. Privacy
“Privacy is dead, deal with it,” Sun MicroSystems CEO Scott McNealy
But like the rattlesnake, it can bite you …
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5. Why Confidentiality Matters
Ethical: Keeping promises; basic value tied to privacy concerns of solitude, autonomy and individuality
Pragmatic: Without confidentiality, respondent may not provide data; worse, may provide inaccurate data
Legal: Required under law
Fundamental to the workings of a democratic system and its respect for the individual. Pragmatically, patients fear not getting it. Without assurances of confidentiality will some patients shy from providing information about potentially embarrassing conditions or activities?
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6. Restricted Access Restricted Data
There is an inverse relationship between restricted data and restricted access: as data restrictions increase, fewer restrictions on access are needed, and vice versa. Some user needs cannot be met with restricted data, however, because the data transformation required to ensure data confidentiality is so extreme that the restricted data are useless for inference purposes. In response, an agency may allow more restricted access to less restricted data. Conversely, to ensure confidentiality, access may have to be so restricted that a legitimate user cannot, as a practical matter, obtain the data. Again in response, an agency may allow less restricted access to more restricted data.
Confidentiality protection shields must be both effective and faithful to the original data.
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7. R-U Confidentiality Map
Data Utility U
Disclosure Risk R
Original Data
Maximum
Tolerable Risk
Released Data
Disclosure Risk: Information about Confidential Items
Data Utility: Information about legitimate objects (DATA)
No Data
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8. R-U Confidentiality Map
Attention from Census
Data Utility U
Disclosure Risk R
Original Data
Maximum
Tolerable Risk
Attention from Transportation Community
Disclosure Risk: Information about Confidential Items
Data Utility: Information about legitimate objects (DATA)
No Data
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9. Questions for Joint Consideration
Does old data decay and so pose less disclosure risk?
Can providing seasonal data increase data utility without increasing disclosure risk?
How can the transportation community be educated about the impact of disclosure limitation?
What specifically causes increased disclosure risk?
How to improve access to Research Data Centers?
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10. Disclosure Limitation for Tables
Coarsening
Aggregate attributes
Suppress some cells
Publish only the marginal totals
Suppress the sensitive cells, plus others as necessary
Perturb some cells
Round
Fuzz
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11. Perturbation Methods Controlled rounding (Cox)
Cyclic perturbation (Duncan & Roehrig)
Stochastically modify cell values in a known way, allowing a Bayesian analysis of cell value distributions
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12. Multiple Sources of Information
Alan Pisarski, Thomas Palumbo
Research and Academic Breakout Group
Session 8
“Mixing apples and oranges produces fruit salad”
“Institutionalize creative quilting” (Nancy McGuckin)
11/15/2018

13. “Institutionalize Creative Quilting”
Quilting is the systematic use of multiple data sources
Creative means drawing on the available resources to address a particular problem
Institutionalize means establishing an apparatus whereby creative quilting can consistently happen
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14. Quilting
ACS can’t provide all transportation planning data needs—national data not be all and end all
Integrate with other data sets to maximize combined utility to support decision making
Complement with NHTS, private sector databases, public domain property data, satellite imagery, etc
Build in dynamic databases that assess changing world—close to real-time data
Need new methodologies to produce a high-quality data quilt (data stitching)
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15. Creative
Constrain focus to particular decision making and policy needs
Search broadly and be imaginative in use of data products
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16. Institutionalize Not left to individual planner/researcher
Identify commonalities and differences among data users, say MPOs
Find representation for users with common needs, say AASHTO
Establish ongoing links with information organizations, say Census
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17. Scenario of Potential Several MPOs have similar data needs
AASHTO solicits data needs and formulates a “quilt” to cover them
AASHTO negotiates with Census for
way to develop disclosure limited data product
Access to Census Research Data Center to validate statistical inferences from original data (“red light”/”green light”)
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18. Opportunities and Threats
Opportunity: Together, Census and Transportation support respective institutional needs for confidentiality and access to quality data
Threat: Divided, Census and Transportation fall to privacy appeals or inability to cope with methodological challenges of disclosure limitation and complex, new data sources
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