Membership Inference Attacks against Machine Learning Models

18 October 2016

Papers citing "Membership Inference Attacks against Machine Learning Models"

50 / 2,058 papers shown

Title
Siloed Federated Learning for Multi-Centric Histopathology Datasets M. Andreux Jean Ogier du Terrail C. Béguier Eric W. Tramel FedML OOD AI4CE 20 113 0 17 Aug 2020
WAFFLe: Weight Anonymized Factorization for Federated Learning Weituo Hao Nikhil Mehta Kevin J Liang Pengyu Cheng Mostafa El-Khamy Lawrence Carin FedML 40 12 0 13 Aug 2020
Data Privacy in IoT Equipped Future Smart Homes A. Khodabakhsh Sule YAYILGAN YILDIRIM 10 2 0 11 Aug 2020
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning Jiahao Ding Jingyi Wang Guannan Liang J. Bi Miao Pan 17 12 0 11 Aug 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 46 443 0 09 Aug 2020
Local Differential Privacy and Its Applications: A Comprehensive Survey Mengmeng Yang Lingjuan Lyu Jun Zhao Tianqing Zhu Kwok-Yan Lam 18 137 0 09 Aug 2020
Improving on-device speaker verification using federated learning with privacy Filip Granqvist M. Seigel Rogier van Dalen Áine Cahill Stephen Shum Matthias Paulik FedML 16 54 0 06 Aug 2020
Data Minimization for GDPR Compliance in Machine Learning Models Abigail Goldsteen Gilad Ezov Ron Shmelkin Micha Moffie Ariel Farkash 8 63 0 06 Aug 2020
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence Tianqing Zhu Dayong Ye Wei Wang Wanlei Zhou Philip S. Yu SyDa 38 125 0 05 Aug 2020
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization Rui Hu Yanmin Gong Yuanxiong Guo FedML 34 55 0 01 Aug 2020
Membership Leakage in Label-Only Exposures Zheng Li Yang Zhang 34 237 0 30 Jul 2020
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs N. Jha Sparsh Mittal Binod Kumar Govardhan Mattela AAML 26 12 0 30 Jul 2020
Learner's Dilemma: IoT Devices Training Strategies in Collaborative Deep Learning Deepti Gupta O. Kayode Smriti Bhatt Maanak Gupta A. Tosun 16 23 0 30 Jul 2020
Privacy-preserving Voice Analysis via Disentangled Representations Ranya Aloufi Hamed Haddadi David E. Boyle DRL 28 58 0 29 Jul 2020
Label-Only Membership Inference Attacks Christopher A. Choquette-Choo Florian Tramèr Nicholas Carlini Nicolas Papernot MIACV MIALM 38 494 0 28 Jul 2020
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 147 178 0 28 Jul 2020
Attacking and Defending Machine Learning Applications of Public Cloud Dou Goodman Xin Hao SILM AAML 27 7 0 27 Jul 2020
Anonymizing Machine Learning Models Abigail Goldsteen Gilad Ezov Ron Shmelkin Micha Moffie Ariel Farkash MIACV 19 5 0 26 Jul 2020
SOTERIA: In Search of Efficient Neural Networks for Private Inference Anshul Aggarwal Trevor E. Carlson Reza Shokri Shruti Tople FedML 27 12 0 25 Jul 2020
Privacy-preserving Artificial Intelligence Techniques in Biomedicine Reihaneh Torkzadehmahani Reza Nasirigerdeh David B. Blumenthal T. Kacprowski M. List ... Harald H. H. W. Schmidt A. Schwalber Christof Tschohl Andrea Wohner Jan Baumbach 26 60 0 22 Jul 2020
How Does Data Augmentation Affect Privacy in Machine Learning? Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu MU 26 1 0 21 Jul 2020
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning S. K. Murakonda Reza Shokri 8 73 0 18 Jul 2020
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Vladimiro González-Zelaya P. Missier Magdalene Ng Aad van Moorsel 39 17 0 17 Jul 2020
Deep Learning Backdoors Shaofeng Li Shiqing Ma Minhui Xue Benjamin Zi Hao Zhao 29 34 0 16 Jul 2020
A Survey of Privacy Attacks in Machine Learning M. Rigaki Sebastian Garcia PILM AAML 39 213 0 15 Jul 2020
Quality Inference in Federated Learning with Secure Aggregation Balázs Pejó G. Biczók FedML 21 22 0 13 Jul 2020
The Trade-Offs of Private Prediction Laurens van der Maaten Awni Y. Hannun 25 22 0 09 Jul 2020
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning Vaikkunth Mugunthan Ravi Rahman Lalana Kagal FedML 21 40 0 08 Jul 2020
Sharing Models or Coresets: A Study based on Membership Inference Attack Hanlin Lu Changchang Liu T. He Shiqiang Wang Kevin S. Chan MIACV FedML 19 15 0 06 Jul 2020
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning Seth Neel Aaron Roth Saeed Sharifi-Malvajerdi MU 17 253 0 06 Jul 2020
Offline Model Guard: Secure and Private ML on Mobile Devices Sebastian P. Bayerl Tommaso Frassetto Patrick Jauernig Korbinian Riedhammer A. Sadeghi T. Schneider Emmanuel Stapf Christian Weinert OffRL 23 45 0 05 Jul 2020
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network Chuan Ma Jun Li Ming Ding Bo Liu Kang Wei J. Weng H. Vincent Poor SyDa 19 19 0 04 Jul 2020
Reducing Risk of Model Inversion Using Privacy-Guided Training Abigail Goldsteen Gilad Ezov Ariel Farkash 33 4 0 29 Jun 2020
Best-Effort Adversarial Approximation of Black-Box Malware Classifiers A. Ali Birhanu Eshete AAML 9 7 0 28 Jun 2020
Subpopulation Data Poisoning Attacks Matthew Jagielski Giorgio Severi Niklas Pousette Harger Alina Oprea AAML SILM 24 114 0 24 Jun 2020
Hermes Attack: Steal DNN Models with Lossless Inference Accuracy Yuankun Zhu Yueqiang Cheng Husheng Zhou Yantao Lu MIACV AAML 39 99 0 23 Jun 2020
With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Linear Regression Models Jialin Wen Benjamin Zi Hao Zhao Minhui Xue Alina Oprea Hai-feng Qian AAML 16 19 0 21 Jun 2020
Rotation-Equivariant Neural Networks for Privacy Protection Hao Zhang Yiting Chen Haotian Ma Xu Cheng Qihan Ren Liyao Xiang Jie Shi Quanshi Zhang 23 3 0 21 Jun 2020
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks Lixin Fan Kam Woh Ng Ce Ju Tianyu Zhang Chang Liu Chee Seng Chan Qiang Yang MIACV 17 63 0 20 Jun 2020
SPEED: Secure, PrivatE, and Efficient Deep learning Arnaud Grivet Sébert Rafael Pinot Martin Zuber Cédric Gouy-Pailler Renaud Sirdey FedML 15 20 0 16 Jun 2020
A One-Pass Private Sketch for Most Machine Learning Tasks Benjamin Coleman Anshumali Shrivastava SyDa 13 4 0 16 Jun 2020
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 28 32 0 16 Jun 2020
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems Sixu Hu Yuan N. Li Xu Liu Yue Liu Zhaomin Wu Bingsheng He FedML 18 54 0 14 Jun 2020
Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski Jonathan R. Ullman Alina Oprea FedML 23 237 0 13 Jun 2020
Understanding Unintended Memorization in Federated Learning Om Thakkar Swaroop Indra Ramaswamy Rajiv Mathews Franccoise Beaufays FedML 22 46 0 12 Jun 2020
On the Effectiveness of Regularization Against Membership Inference Attacks Yigitcan Kaya Sanghyun Hong Tudor Dumitras 43 27 0 09 Jun 2020
Stealing Deep Reinforcement Learning Models for Fun and Profit Kangjie Chen Shangwei Guo Tianwei Zhang Xiaofei Xie Yang Liu MLAU MIACV OffRL 24 45 0 09 Jun 2020
Trade-offs between membership privacy & adversarially robust learning Jamie Hayes SILM 30 3 0 08 Jun 2020
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing T. Ryffel Pierre Tholoniat D. Pointcheval Francis R. Bach FedML 28 94 0 08 Jun 2020
LDP-Fed: Federated Learning with Local Differential Privacy Stacey Truex Ling Liu Ka-Ho Chow Mehmet Emre Gursoy Wenqi Wei FedML 12 387 0 05 Jun 2020