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Why Train More? Effective and Efficient Membership Inference via
  Memorization

Why Train More? Effective and Efficient Membership Inference via Memorization

12 October 2023
Jihye Choi
Shruti Tople
Varun Chandrasekaran
Somesh Jha
    TDI
    FedML
ArXivPDFHTML

Papers citing "Why Train More? Effective and Efficient Membership Inference via Memorization"

5 / 5 papers shown
Title
SoK: Memorisation in machine learning
SoK: Memorisation in machine learning
Dmitrii Usynin
Moritz Knolle
Georgios Kaissis
22
1
0
06 Nov 2023
CaPC Learning: Confidential and Private Collaborative Learning
CaPC Learning: Confidential and Private Collaborative Learning
Christopher A. Choquette-Choo
Natalie Dullerud
Adam Dziedzic
Yunxiang Zhang
S. Jha
Nicolas Papernot
Xiao Wang
FedML
70
57
0
09 Feb 2021
Extracting Training Data from Large Language Models
Extracting Training Data from Large Language Models
Nicholas Carlini
Florian Tramèr
Eric Wallace
Matthew Jagielski
Ariel Herbert-Voss
...
Tom B. Brown
D. Song
Ulfar Erlingsson
Alina Oprea
Colin Raffel
MLAU
SILM
290
1,824
0
14 Dec 2020
When is Memorization of Irrelevant Training Data Necessary for
  High-Accuracy Learning?
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?
Gavin Brown
Mark Bun
Vitaly Feldman
Adam D. Smith
Kunal Talwar
253
93
0
11 Dec 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models
Systematic Evaluation of Privacy Risks of Machine Learning Models
Liwei Song
Prateek Mittal
MIACV
196
358
0
24 Mar 2020
1