Covert Attacks on Machine Learning Training in Passively Secure MPC

21 May 2025

Papers citing "Covert Attacks on Machine Learning Training in Passively Secure MPC"

35 / 35 papers shown

Title
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 48 12 0 08 Jul 2023
A Note On Interpreting Canary Exposure Matthew Jagielski 47 5 0 31 May 2023
To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods Dawen Zhang Shidong Pan Thong Hoang Zhenchang Xing Mark Staples Xiwei Xu Lina Yao Qinghua Lu Liming Zhu MU 32 18 0 07 Feb 2023
Editing Models with Task Arithmetic Gabriel Ilharco Marco Tulio Ribeiro Mitchell Wortsman Suchin Gururangan Ludwig Schmidt Hannaneh Hajishirzi Ali Farhadi KELM MoMe MU 97 462 0 08 Dec 2022
Amplifying Membership Exposure via Data Poisoning Yufei Chen Chao Shen Yun Shen Cong Wang Yang Zhang AAML 62 28 0 01 Nov 2022
The Privacy Onion Effect: Memorization is Relative Nicholas Carlini Matthew Jagielski Chiyuan Zhang Nicolas Papernot Andreas Terzis Florian Tramèr PILM MIACV 83 104 0 21 Jun 2022
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets Florian Tramèr Reza Shokri Ayrton San Joaquin Hoang Minh Le Matthew Jagielski Sanghyun Hong Nicholas Carlini MIACV 68 112 0 31 Mar 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 62 162 0 13 Jan 2022
Membership Inference Attacks From First Principles Nicholas Carlini Steve Chien Milad Nasr Shuang Song Andreas Terzis Florian Tramèr MIACV MIALM 38 663 0 07 Dec 2021
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 83 183 0 06 Dec 2021
Enhanced Membership Inference Attacks against Machine Learning Models Jiayuan Ye Aadyaa Maddi S. K. Murakonda Vincent Bindschaedler Reza Shokri MIALM MIACV 37 242 0 18 Nov 2021
CrypTen: Secure Multi-Party Computation Meets Machine Learning Brian Knott Shobha Venkataraman Awni Y. Hannun Shubho Sengupta Mark Ibrahim Laurens van der Maaten 29 352 0 02 Sep 2021
Secure Quantized Training for Deep Learning Marcel Keller Ke Sun MQ 40 66 0 01 Jul 2021
Handcrafted Backdoors in Deep Neural Networks Sanghyun Hong Nicholas Carlini Alexey Kurakin 63 74 0 08 Jun 2021
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 61 187 0 22 Apr 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 94 219 0 11 Jan 2021
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 364 1,868 0 14 Dec 2020
Subpopulation Data Poisoning Attacks Matthew Jagielski Giorgio Severi Niklas Pousette Harger Alina Oprea AAML SILM 34 117 0 24 Jun 2020
On Adversarial Bias and the Robustness of Fair Machine Learning Hong Chang Ta Duy Nguyen S. K. Murakonda Ehsan Kazemi Reza Shokri FaML OOD FedML 21 51 0 15 Jun 2020
Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski Jonathan R. Ullman Alina Oprea FedML 31 240 0 13 Jun 2020
Poisoning Attacks on Algorithmic Fairness David Solans Battista Biggio Carlos Castillo AAML 15 81 0 15 Apr 2020
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning Sameer Wagh Shruti Tople Fabrice Benhamouda E. Kushilevitz Prateek Mittal T. Rabin FedML 43 297 0 05 Apr 2020
Reverse-Engineering Deep ReLU Networks David Rolnick Konrad Paul Kording 11 103 0 02 Oct 2019
CrypTFlow: Secure TensorFlow Inference Nishant Kumar Mayank Rathee Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 105 238 0 16 Sep 2019
High Accuracy and High Fidelity Extraction of Neural Networks Matthew Jagielski Nicholas Carlini David Berthelot Alexey Kurakin Nicolas Papernot MLAU MIACV 53 377 0 03 Sep 2019
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 239 1,044 0 29 Nov 2018
Private Machine Learning in TensorFlow using Secure Computation Morten Dahl Jason V. Mancuso Yann Dupis Ben Decoste Morgan Giraud Ian Livingstone Justin Patriquin Gavin Uhma FedML 21 76 0 18 Oct 2018
How To Backdoor Federated Learning Eugene Bagdasaryan Andreas Veit Yiqing Hua D. Estrin Vitaly Shmatikov SILM FedML 50 1,892 0 02 Jul 2018
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks Ali Shafahi Wenjie Huang Mahyar Najibi Octavian Suciu Christoph Studer Tudor Dumitras Tom Goldstein AAML 56 1,080 0 03 Apr 2018
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning Matthew Jagielski Alina Oprea Battista Biggio Chang-rui Liu Cristina Nita-Rotaru Yue Liu AAML 42 757 0 01 Apr 2018
Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks Octavian Suciu R. Marginean Yigitcan Kaya Hal Daumé Tudor Dumitras AAML 55 287 0 19 Mar 2018
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning Xinyun Chen Chang-rui Liu Yue Liu Kimberly Lu D. Song AAML SILM 62 1,822 0 15 Dec 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 54 1,754 0 22 Aug 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 191 4,075 0 18 Oct 2016
Poisoning Attacks against Support Vector Machines Battista Biggio B. Nelson Pavel Laskov AAML 48 1,580 0 27 Jun 2012