Privacy for Bayesian modelling
1 hour 12 mins,
347.98 MB,
WebM
640x360,
29.97 fps,
44100 Hz,
659.87 kbits/sec
Share this media item:
Embed this media item:
Embed this media item:
About this item
Description: |
Charest, AS (Université Laval)
Thursday 28th July 2016 - 15:30 to 16:30 |
---|
Created: | 2016-07-28 18:15 |
---|---|
Collection: | Data Linkage and Anonymisation |
Publisher: | Isaac Newton Institute |
Copyright: | Charest, AS |
Language: | eng (English) |
Distribution: | World (downloadable) |
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. |
---|
Available Formats
Format | Quality | Bitrate | Size | |||
---|---|---|---|---|---|---|
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 | |
Auto | (Allows browser to choose a format it supports) |