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

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

9 February 2021

Maximilian Henzl

Lukas Daniel Klausner

Peter Kieseberg

Matthias Zeppelzauer

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

6 / 6 papers shown

Title
Multi-Objective Optimization-Based Anonymization of Structured Data for Machine Learning Application Yusi Wei Hande Y. Benson Joseph K. Agor Muge Capan 28 0 0 02 Jan 2025
Synthetic Data: Revisiting the Privacy-Utility Trade-off Fatima Jahan Sarmin Atiquer Rahman Sarkar Yang Wang Noman Mohammed 32 3 0 09 Jul 2024
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning Yumeki Goto Tomoya Matsumoto Hamada Rizk Naoto Yanai Hirozumi Yamaguchi 33 6 0 14 May 2023
Comparison of machine learning models applied on anonymized data with different techniques Judith Sáinz-Pardo Díaz Á. García 16 5 0 12 May 2023
Energy cost and machine learning accuracy impact of k-anonymisation and synthetic data techniques Pepijn de Reus Ana Oprescu Koen van Elsen 14 3 0 11 May 2023
Anonymous Bandits for Multi-User Systems Hossein Esfandiari Vahab Mirrokni Jon Schneider PICV 21 0 0 21 Oct 2022