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$k$-Anonymity in Practice: How Generalisation and Suppression Affect
  Machine Learning Classifiers
v1v2 (latest)

kkk-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers

9 February 2021
D. Slijepcevic
Maximilian Henzl
Lukas Daniel Klausner
Tobias Dam
Peter Kieseberg
Matthias Zeppelzauer
ArXiv (abs)PDFHTML

Papers citing "$k$-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers"

3 / 3 papers shown
Title
Multi-Objective Optimization-Based Anonymization of Structured Data for Machine Learning Application
Multi-Objective Optimization-Based Anonymization of Structured Data for Machine Learning Application
Yusi Wei
Hande Y. Benson
Joseph K. Agor
Muge Capan
62
0
0
02 Jan 2025
The Limits of Differential Privacy (and its Misuse in Data Release and
  Machine Learning)
The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)
J. Domingo-Ferrer
David Sánchez
Alberto Blanco-Justicia
82
108
0
04 Nov 2020
XGBoost: A Scalable Tree Boosting System
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
Carlos Guestrin
807
38,961
0
09 Mar 2016
1