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"Amnesia" - A Selection of Machine Learning Models That Can Forget User Data Very Fast

Summary: Amnesia: efficient decremental update algorithms that remove a user’s contribution from trained ML models without re-accessing original training data, enabling fast compliance with right-to-be-forgotten. Rust implementations for four common ML methods show orders-of-magnitude speedups on nine real datasets and outline Differential Dataflow parallelization plus limitations. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
369
Venue
CIDR
Year
2020
Pagerank
5.5051607e-05
Overall Rank
5,433 | 62.21%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
34 Similarity Search in High Dimensions via Hashing 1999 VLDB 0.00076637636
522 Differential dataflow 2013 CIDR 0.00021099241
2,172 Spinning Fast Iterative Data Flows 2012 VLDB 9.3706587e-05
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