A Survey of Machine Unlearning
- URL: http://arxiv.org/abs/2209.02299v3
- Date: Thu, 8 Sep 2022 16:52:04 GMT
- Title: A Survey of Machine Unlearning
- Authors: Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung
Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen
- Abstract summary: Recent regulations require that private information about a user can be removed from computer systems in general and from ML models in particular upon request.
This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data.
We seek to provide a thorough investigation of machine unlearning in its definitions, scenarios, mechanisms, and applications.
- Score: 43.272767023563254
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Computer systems hold a large amount of personal data over decades. On the
one hand, such data abundance allows breakthroughs in artificial intelligence
(AI), especially machine learning (ML) models. On the other hand, it can
threaten the privacy of users and weaken the trust between humans and AI.
Recent regulations require that private information about a user can be removed
from computer systems in general and from ML models in particular upon request
(e.g. the "right to be forgotten"). While removing data from back-end databases
should be straightforward, it is not sufficient in the AI context as ML models
often "remember" the old data. Existing adversarial attacks proved that we can
learn private membership or attributes of the training data from the trained
models. This phenomenon calls for a new paradigm, namely machine unlearning, to
make ML models forget about particular data. It turns out that recent works on
machine unlearning have not been able to solve the problem completely due to
the lack of common frameworks and resources. In this survey paper, we seek to
provide a thorough investigation of machine unlearning in its definitions,
scenarios, mechanisms, and applications. Specifically, as a categorical
collection of state-of-the-art research, we hope to provide a broad reference
for those seeking a primer on machine unlearning and its various formulations,
design requirements, removal requests, algorithms, and uses in a variety of ML
applications. Furthermore, we hope to outline key findings and trends in the
paradigm as well as highlight new areas of research that have yet to see the
application of machine unlearning, but could nonetheless benefit immensely. We
hope this survey provides a valuable reference for ML researchers as well as
those seeking to innovate privacy technologies. Our resources are at
https://github.com/tamlhp/awesome-machine-unlearning.
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