Making AI forget you: Data deletion in machine learning T ONY G - PowerPoint PPT Presentation

Nov 18, 2022 •434 likes •561 views

Making AI forget you: Data deletion in machine learning T ONY G INART M ELODY G UAN , G REG V ALIANT , J AMES Z OU Advances in Neural Information Processing Systems December 12, 2019 AI systems today... Data Algorithm Model Users AI systems

Making AI forget you: Data deletion in machine learning T ONY G INART M ELODY G UAN , G REG V ALIANT , J AMES Z OU Advances in Neural Information Processing Systems December 12, 2019
AI systems today... Data Algorithm Model Users
AI systems today... Data Algorithm Model deletion Users
AI systems today... Updated Data Algorithm Model Model deletion Users Deletion Op
Deletion requests in the wild... EMAIL ---- UK BIOBANK ---- Subject: UK Biobank Application [REDACTED], Participant Withdrawal Notification [REDACTED] Dear Researcher, As you are aware, participants are free to withdraw form the UK Biobank at any time and request that their data no longer be used. Since our last review, some participants involved with Application [REDACTED] have requested that their data should longer be used.
Contributions 1) Define deletion in ML system and notion of efficient deletion 2) Propose general principles for co-design of ML algorithms and deletion operations 3) Introduce deletion efficient unsupervised learning
What is “data deletion” for an ML system? Informal definition: Deleting a data point from a trained ML model means updating the model as if this point had never existed.
What is “deletion efficiency” for an ML system? ▪ Setting: online deletion requests from users ▪ Figure-of-Merit: amortized computation X X X ...
Toolbox for deletion efficient ML ▪ Linearity : fast O(1) deletion with respect to n data points ▪ Laziness : E.g. nearest neighbors ▪ Modularity : Control dependency from data to parameters ▪ Quantization : Efficiently check if deletion matters
State of progress Supervised learning: ▪ Linear regressions/models ▪ Non-parameteric (k-NN) ▪ Incremental SVMs Unsupervised learning : ▪ 1) Quantized k-means ▪ 2) Divide-and-Conquer k-means
State of progress Supervised learning: ▪ Linear regressions/models ▪ Non-parameteric (k-NN) ▪ Incremental SVMs Unsupervised learning : ▪ 1) Quantized k-means ▪ 2) Divide-and-Conquer k-means 100X faster deletion without loss of clustering quality
Next steps in deletion efficient ML Models: Want to know more? ▪ Decision trees/forests ▪ Artificial neural networks Poster session @ 5pm #123, East Exhibition Hall B + C Settings: ▪ Approximate deletions Thank you! ▪ Adversarial requests Happy to chat more: Paradigms: tginart@stanford.edu ▪ Reinforcement learning ▪ Representation/embedding learning

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