Making AI Forget You: Data Deletion in Machine Learning

Item

Title: Making AI Forget You: Data Deletion in Machine Learning
Creator: Antonio A. Ginart; Melody Y. Guan; Gregory Valiant; James Zou
Is Referenced By: C8RGKC5H
Abstract: Intense recent discussions have focused on how to provide individuals with control
over when their data can and cannot be used — the EU’s Right To Be Forgotten
regulation is an example of this effort. In this paper we initiate a framework studying
what to do when it is no longer permissible to deploy models derivative from specific
user data. In particular, we formulate the problem of efficiently deleting individual
data points from trained machine learning models. For many standard ML models,
the only way to completely remove an individual’s data is to retrain the whole model
from scratch on the remaining data, which is often not computationally practical.
We investigate algorithmic principles that enable efficient data deletion in ML.
For the specific setting of k-means clustering, we propose two provably efficient
deletion algorithms which achieve an average of over 100×improvement in deletion
efficiency across 6 datasets, while producing clusters of comparable statistical
quality to a canonical k-means++ baseline.
Date: 2019
presented at: 33rd Conference on Neural Information Processing Systems
Publisher: Vancouver
Language: English
uri: https://papers.nips.cc/paper/8611-making-ai-forget-you-data-deletion-in-machine-learning.pdf
Date Submitted: 2021-09-27T16:47:53Z
is compiled by: ARCANES; gabarielleg
is in semantic relation with: hypertrucage; Deep Fakes; machine learning; Régulation; données; computationnel
has source: Visio complète

Item sets: Groupe Zotero Arcanes

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