Privacy for Bayesian modelling
1 hour 12 mins,
261.08 MB,
iPod Video
480x270,
29.97 fps,
44100 Hz,
495.07 kbits/sec
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About this item
| Description: |
Charest, AS (Université Laval)
Thursday 28th July 2016 - 15:30 to 16:30 |
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| Created: | 2016-07-28 18:15 |
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| Collection: | Data Linkage and Anonymisation |
| Publisher: | Isaac Newton Institute |
| Copyright: | Charest, AS |
| 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: | The literature now contains a large set of methods to privately estimate parameters from a classical statistical model, or to conduct a data mining or machine learning task. However, little is known about how to perform Bayesian statistics privately. In this talk, I will share my thoughts, and a few results, about ways in which Bayesian modelling could be performed to offer some privacy guarantee. In particular, I will discuss some attempts at sampling from posterior predictive distributions under the constraint of differential privacy (DP). I will also discuss empirical differential privacy, a criterion designed to estimate the DP privacy level offered by a certain Bayesian model, and present some recent results on the meaning and limits of this privacy measure. A lot of what I will present is work in progress, and I am hoping that some of you may want to collaborate with me on this research topic. |
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| MPEG-4 Video | 640x360 | 1.92 Mbits/sec | 1.02 GB | View | Download | |
| WebM | 640x360 | 659.87 kbits/sec | 347.98 MB | View | Download | |
| iPod Video * | 480x270 | 495.07 kbits/sec | 261.08 MB | View | Download | |
| MP3 | 44100 Hz | 252.21 kbits/sec | 133.00 MB | Listen | Download | |
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