On Inferring Training Data Attributes in Machine Learning Models

Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy

Hanchuan Peng, Fuhui Long, and Chris Ding. 2005 · 2005

Earlier work this paper cites.

Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. In USENIX Security Symposium . 17–32

Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart. 2014 · 2014

Earlier work this paper cites.

Stealing Machine Learning Models via Prediction APIs. In USENIX Security Symposium . 601–618

Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016 · 2016

Earlier work this paper cites.

Participatory cultural mapping based on collective behavior data in location-based social networks

Dingqi Yang, Daqing Zhang, and Bingqing Qu. 2016 · 2016

Cited alongside, same era.

Membership inference attacks against machine learning models. In IEEE Symposium on Security and Privacy (SP) . IEEE, 3–18

Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017

Cited alongside, same era.

Understanding membership inferences on well-generalized learning models

Original

Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen. 2018 · 2018

Cited alongside, same era.

Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st Computer Security Foundations Symposium (CSF) . IEEE, 268–282

Original

Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018

Later among the works it cites.

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Ahmed Salem, Yang Zhang, Mathias Humbert, Mario Fritz, and Michael Backes. 2019 · 2019

Closest in time.

Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning

Original

Mohammad Yaghini, Bogdan Kulynych, and Carmela Troncoso. 2019 · 2019

Closest in time.