Assessing Re-identification Risk in Sample Microdata
28 mins 4 secs,
408.77 MB,
MPEG-4 Video
640x360,
29.97 fps,
44100 Hz,
1.94 Mbits/sec
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About this item
| Description: |
Shlomo, N (University of Manchester)
Thursday 8th December 2016 - 16:30 to 17:00 |
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| Created: | 2016-12-19 12:24 |
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| Collection: | Data Linkage and Anonymisation |
| Publisher: | Isaac Newton Institute |
| Copyright: | Shlomo, N |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-author: Chris Skinner
Abstract: Disclosure risk occurs when there is a high probability that an intruder can identify an individual in released sample microdata and confidential information may be revealed. A probabilistic modelling framework based on the Poisson log-linear model is used for quantifying disclosure risk in terms of population uniqueness when population counts are unknown. This method does not account for measurement error arising either naturally from survey processes or purposely introduced as a perturbative disclosure limitation technique. The probabilistic modelling framework for assessing disclosure risk is expanded to take into account the misclassification/ perturbation and demonstrated on sample microdata which has undergone perturbation procedures. Finally, we adapt the probabilistic modelling framework to assess the disclosure risk of samples from sub-populations and show some initial results. |
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