Membership Inference Attacks against Machine Learning Models

18 October 2016

Papers citing "Membership Inference Attacks against Machine Learning Models"

50 / 2,056 papers shown

Title
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements Xiaoyi Chen A. Salem Dingfan Chen Michael Backes Shiqing Ma Qingni Shen Zhonghai Wu Yang Zhang SILM 32 228 0 01 Jun 2020
On the Difficulty of Membership Inference Attacks Shahbaz Rezaei Xin Liu MIACV 22 13 0 27 May 2020
Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning Renuga Kanagavelu Zengxiang Li J. Samsudin Yechao Yang Feng Yang Rick Siow Mong Goh Mervyn Cheah Praewpiraya Wiwatphonthana K. Akkarajitsakul Shangguang Wang FedML 17 88 0 25 May 2020
Secure and Differentially Private Bayesian Learning on Distributed Data Yeongjae Gil Xiaoqian Jiang Miran Kim Junghye Lee 8 2 0 22 May 2020
Revisiting Membership Inference Under Realistic Assumptions Bargav Jayaraman Lingxiao Wang Katherine Knipmeyer Quanquan Gu David Evans 24 147 0 21 May 2020
Scalable Privacy-Preserving Distributed Learning D. Froelicher J. Troncoso-Pastoriza Apostolos Pyrgelis Sinem Sav João Sá Sousa Jean-Philippe Bossuat Jean-Pierre Hubaux FedML 22 68 0 19 May 2020
VerifyTL: Secure and Verifiable Collaborative Transfer Learning Zhuo Ma Jianfeng Ma Yinbin Miao Ximeng Liu Wei Zheng K. Choo R. Deng AAML 16 3 0 18 May 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 33 83 0 18 May 2020
DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks Hongwei Huang Weiqi Luo Guoqiang Zeng J. Weng Yue Zhang Anjia Yang AAML 17 24 0 16 May 2020
Differentially Private ADMM for Convex Distributed Learning: Improved Accuracy via Multi-Step Approximation Zonghao Huang Yanmin Gong FedML 11 11 0 16 May 2020
Perturbing Inputs to Prevent Model Stealing J. Grana AAML SILM 24 5 0 12 May 2020
A Secure Federated Learning Framework for 5G Networks Yi Liu Jia-Jie Peng Jiawen Kang Abdullah M. Iliyasu Dusit Niyato A. El-latif FedML 17 195 0 12 May 2020
Defending Model Inversion and Membership Inference Attacks via Prediction Purification Ziqi Yang Bin Shao Bohan Xuan E. Chang Fan Zhang AAML 25 71 0 08 May 2020
MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation Sanjay Kariyappa A. Prakash Moinuddin K. Qureshi AAML 32 146 0 06 May 2020
When Machine Unlearning Jeopardizes Privacy Min Chen Zhikun Zhang Tianhao Wang Michael Backes Mathias Humbert Yang Zhang MIACV 36 218 0 05 May 2020
Secure Deep Graph Generation with Link Differential Privacy Carl Yang Haonan Wang Ke Zhang Liang Chen Lichao Sun 30 40 0 01 May 2020
Differentially Private Federated Learning with Laplacian Smoothing Zhicong Liang Bao Wang Quanquan Gu Stanley Osher Yuan Yao FedML 20 7 0 01 May 2020
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms Mahdi Haghifam Jeffrey Negrea Ashish Khisti Daniel M. Roy Gintare Karolina Dziugaite 34 105 0 27 Apr 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 78 25 0 27 Apr 2020
Privacy in Deep Learning: A Survey Fatemehsadat Mirshghallah Mohammadkazem Taram Praneeth Vepakomma Abhishek Singh Ramesh Raskar H. Esmaeilzadeh FedML 19 136 0 25 Apr 2020
Enhancing Privacy via Hierarchical Federated Learning A. Wainakh Alejandro Sánchez Guinea Tim Grube M. Mühlhäuser FedML 28 45 0 23 Apr 2020
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning Wenqi Wei Ling Liu Margaret Loper Ka-Ho Chow Mehmet Emre Gursoy Stacey Truex Yanzhao Wu FedML 26 147 0 22 Apr 2020
Have you forgotten? A method to assess if machine learning models have forgotten data Xiao Liu Sotirios A. Tsaftaris FedML OOD MU 21 26 0 21 Apr 2020
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments Fan Mo Ali Shahin Shamsabadi Kleomenis Katevas Soteris Demetriou Ilias Leontiadis Andrea Cavallaro Hamed Haddadi FedML 18 176 0 12 Apr 2020
PrivEdge: From Local to Distributed Private Training and Prediction Ali Shahin Shamsabadi Adria Gascon Hamed Haddadi Andrea Cavallaro 34 19 0 12 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 33 295 0 05 Apr 2020
Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks Di Gao Cheng Zhuo 14 4 0 05 Apr 2020
Information Leakage in Embedding Models Congzheng Song A. Raghunathan MIACV 24 263 0 31 Mar 2020
Concentrated Differentially Private and Utility Preserving Federated Learning Rui Hu Yuanxiong Guo Yanmin Gong FedML 41 12 0 30 Mar 2020
Differentially Private Federated Learning for Resource-Constrained Internet of Things Rui Hu Yuanxiong Guo E. Ratazzi Yanmin Gong FedML 33 17 0 28 Mar 2020
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy Fatemehsadat Mireshghallah Mohammadkazem Taram A. Jalali Ahmed T. Elthakeb Dean Tullsen H. Esmaeilzadeh 14 12 0 26 Mar 2020
Corella: A Private Multi Server Learning Approach based on Correlated Queries H. Ehteram M. Maddah-ali Mahtab Mirmohseni 14 0 0 26 Mar 2020
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks Samuel Deng Sanjam Garg S. Jha Saeed Mahloujifar Mohammad Mahmoody Abhradeep Thakurta 20 3 0 26 Mar 2020
Learn to Forget: Machine Unlearning via Neuron Masking Yang Liu Zhuo Ma Ximeng Liu Jian-wei Liu Zhongyuan Jiang Jianfeng Ma Philip Yu K. Ren MU 22 61 0 24 Mar 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 360 0 24 Mar 2020
Survey of Personalization Techniques for Federated Learning V. Kulkarni Milind Kulkarni Aniruddha Pant FedML 182 327 0 19 Mar 2020
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks Erwin Quiring Konrad Rieck AAML 54 70 0 19 Mar 2020
The Future of Digital Health with Federated Learning Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari H. Roth ... Ronald M. Summers Andrew Trask Daguang Xu Maximilian Baust M. Jorge Cardoso OOD 174 1,713 0 18 Mar 2020
ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition S. Bian Tianchen Wang Masayuki Hiromoto Yiyu Shi Takashi Sato FedML 29 30 0 11 Mar 2020
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion Qinqing Zheng Jinshuo Dong Qi Long Weijie J. Su FedML 17 23 0 10 Mar 2020
Towards Probabilistic Verification of Machine Unlearning David M. Sommer Liwei Song Sameer Wagh Prateek Mittal AAML 13 71 0 09 Mar 2020
TEDL: A Text Encryption Method Based on Deep Learning Xiang Li Peng Wang FedML 19 3 0 09 Mar 2020
Dynamic Backdoor Attacks Against Machine Learning Models A. Salem Rui Wen Michael Backes Shiqing Ma Yang Zhang AAML 48 271 0 07 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 204 436 0 04 Mar 2020
Marketplace for AI Models Abhishek Kumar Benjamin Finley Tristan Braud Sasu Tarkoma Pan Hui DiffM 18 14 0 03 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 72 63 0 02 Mar 2020
Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees Aleksei Triastcyn Boi Faltings 17 5 0 02 Mar 2020
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization Kang Wei Jun Li Ming Ding Chuan Ma Hang Su Bo Zhang H. Vincent Poor FedML 25 11 0 29 Feb 2020
Membership Inference Attacks and Defenses in Classification Models Jiacheng Li Ninghui Li Bruno Ribeiro 22 34 0 27 Feb 2020
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective Yilun Jin Xiguang Wei Yang Liu Qiang Yang FedML 22 63 0 26 Feb 2020