Characterizing Machine Unlearning through Definitions and Implementations

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• Nicolas Papernot, University of Toronto and Vector Institute
• Thursday 29 February 2024, 14:00-15:00
• Webinar & FW11, Computer Laboratory, William Gates Building..

If you have a question about this talk, please contact Hridoy Sankar Dutta.

The talk presents open problems in the study of machine unlearning. The need for machine unlearning, i.e., obtaining a model one would get without training on a subset of data, arises from privacy legislation and as a potential solution to data poisoning or copyright claims. The first part of the talk discusses approaches that provide exact unlearning: these approaches output the same distribution of models as would have been obtained by training without the subset of data to be unlearned in the first place. While such approaches can be computationally expensive, we discuss why it is difficult to relax the guarantee they provide to pave the way for more efficient approaches. The second part of the talk asks if we can verify unlearning. Here we show how an entity can claim plausible deniability when challenged about an unlearning request that was claimed to be processed, and conclude that at the level of model weights, being unlearnt is not always a well-defined property. Instead, unlearning is an algorithmic property.

RECORDING : Please note, this event will be recorded and will be available after the event for an indeterminate period under a CC BY -NC-ND license. Audience members should bear this in mind before joining the webinar or asking questions.

https://cam-ac-uk.zoom.us/j/82112795708?pwd=VFBTVjI1YkRqMXY5MEpRcXYzdmN6QT09

Meeting ID: 821 1279 5708 Passcode: 468381

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This talk is part of the Computer Laboratory Security Seminar series.

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