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

v1v2 (latest)

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

4 June 2018

Mathias Humbert

Mario Fritz

Michael Backes

ArXiv (abs)PDF HTML

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

18 / 518 papers shown

Title
Privacy Risks of Securing Machine Learning Models against Adversarial ExamplesConference on Computer and Communications Security (CCS), 2019 Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 175 275 0 24 May 2019
The Audio Auditor: User-Level Membership Inference in Internet of Things Voice ServicesProceedings on Privacy Enhancing Technologies (PoPETs), 2019 Yuantian Miao Minhui Xue Chao Chen Lei Pan Jinchao Zhang Benjamin Zi Hao Zhao Dali Kaafar Yang Xiang 459 42 0 17 May 2019
Language in Our Time: An Empirical Analysis of HashtagsThe Web Conference (WWW), 2019 Yang Zhang 175 27 0 11 May 2019
Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? Sorami Hisamoto Matt Post Kevin Duh MIACV SLR 208 120 0 11 Apr 2019
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning A. Salem Apratim Bhattacharyya Michael Backes Mario Fritz Yang Zhang FedML AAML MIACV 268 278 0 01 Apr 2019
How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNNAsia-Pacific Computer Systems Architecture Conference (APCSAC), 2019 Zheng Li Chengyu Hu Yang Zhang Shanqing Guo AAML 177 192 0 05 Mar 2019
Evaluating Differentially Private Machine Learning in Practice Bargav Jayaraman David Evans 236 7 0 24 Feb 2019
Measuring Membership Privacy on Aggregate Location Time-Series Apostolos Pyrgelis Carmela Troncoso Emiliano De Cristofaro 152 25 0 20 Feb 2019
Stealing Neural Networks via Timing Side Channels Vasisht Duddu D. Samanta D. V. Rao V. Balas AAML MLAU FedML 279 147 0 31 Dec 2018
Knockoff Nets: Stealing Functionality of Black-Box Models Tribhuvanesh Orekondy Bernt Schiele Mario Fritz MLAU 239 593 0 06 Dec 2018
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning Muhammad Shayan Clement Fung Chris J. M. Yoon Ivan Beschastnikh FedML 147 88 0 24 Nov 2018
FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network PredictionsComputer Vision and Pattern Recognition (CVPR), 2018 Shaohua Li Kaiping Xue Chenkai Ding Xindi Gao David S. L. Wei Tao Wan F. Wu 130 80 0 20 Nov 2018
Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference Faiq Khalid Muhammad Abdullah Hanif Semeen Rehman Mohamed Bennai AAML 117 23 0 05 Nov 2018
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service L. Hanzlik Yang Zhang Kathrin Grosse A. Salem Maximilian Augustin Michael Backes Mario Fritz OffRL 203 114 0 01 Aug 2018
Privacy-preserving Machine Learning through Data Obfuscation Tianwei Zhang Zecheng He R. Lee 198 85 0 05 Jul 2018
Killing four birds with one Gaussian process: the relation between different test-time attacks Kathrin Grosse M. Smith Michael Backes AAML 162 2 0 06 Jun 2018
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning Tribhuvanesh Orekondy Seong Joon Oh Yang Zhang Bernt Schiele Mario Fritz PICV FedML 653 40 0 15 May 2018
Towards Plausible Graph Anonymization Yang Zhang Mathias Humbert Bartlomiej Surma Praveen Manoharan Jilles Vreeken Michael Backes 222 21 0 15 Nov 2017