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The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

22 February 2018

Papers citing "The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks"

50 / 441 papers shown

Title
Remember What You Want to Forget: Algorithms for Machine Unlearning Ayush Sekhari Jayadev Acharya Gautam Kamath A. Suresh FedML MU 94 317 0 04 Mar 2021
Wide Network Learning with Differential Privacy Huanyu Zhang Ilya Mironov Meisam Hejazinia 86 27 0 01 Mar 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 169 116 0 25 Feb 2021
Measuring Data Leakage in Machine-Learning Models with Fisher Information Awni Y. Hannun Chuan Guo Laurens van der Maaten FedML MIACV 101 58 0 23 Feb 2021
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts Soravit Changpinyo P. Sharma Nan Ding Radu Soricut VLM 538 1,143 0 17 Feb 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 120 243 0 12 Feb 2021
Node-Level Membership Inference Attacks Against Graph Neural Networks Xinlei He Rui Wen Yixin Wu Michael Backes Yun Shen Yang Zhang 139 98 0 10 Feb 2021
CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo Natalie Dullerud Adam Dziedzic Yunxiang Zhang S. Jha Nicolas Papernot Xiao Wang FedML 130 58 0 09 Feb 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu Sivakanth Gopi Janardhan Kulkarni Y. Lee J. Shen U. Tantipongpipat FedML 109 42 0 05 Feb 2021
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models Yugeng Liu Rui Wen Xinlei He A. Salem Zhikun Zhang Michael Backes Emiliano De Cristofaro Mario Fritz Yang Zhang AAML 100 133 0 04 Feb 2021
Dopamine: Differentially Private Federated Learning on Medical Data Mohammad Malekzadeh Burak Hasircioglu N. Mital K. Katarya M. E. Ozfatura Deniz Gündüz OOD FedML 101 51 0 27 Jan 2021
Property Inference From Poisoning Melissa Chase Esha Ghosh Saeed Mahloujifar MIACV 90 83 0 26 Jan 2021
Fidelity and Privacy of Synthetic Medical Data O. Mendelevitch M. Lesh 93 30 0 18 Jan 2021
Membership Inference Attack on Graph Neural Networks Iyiola E. Olatunji Wolfgang Nejdl Megha Khosla AAML 125 102 0 17 Jan 2021
Training Data Leakage Analysis in Language Models Huseyin A. Inan Osman Ramadan Lukas Wutschitz Daniel Jones Victor Rühle James Withers Robert Sim MIACV PILM 98 9 0 14 Jan 2021
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times Yao Fu Yipeng Zhou Di Wu Shui Yu Yonggang Wen Chao Li FedML 62 10 0 11 Jan 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 173 226 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 Basel Alomair Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 568 1,966 0 14 Dec 2020
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? Gavin Brown Mark Bun Vitaly Feldman Adam D. Smith Kunal Talwar 316 103 0 11 Dec 2020
Modifying Memories in Transformer Models Chen Zhu A. S. Rawat Manzil Zaheer Srinadh Bhojanapalli Daliang Li Felix X. Yu Sanjiv Kumar KELM 123 203 0 01 Dec 2020
Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis J. Scheibner J. Raisaro J. Troncoso-Pastoriza M. Ienca J. Fellay E. Vayena Jean-Pierre Hubaux 46 77 0 27 Oct 2020
FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries Seng Pei Liew Tsubasa Takahashi MIACV FedML 73 9 0 27 Oct 2020
Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis Xudong Pan Mi Zhang Yifan Yan Jiaming Zhu Zhemin Yang AAML 67 22 0 26 Oct 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 361 707 0 19 Oct 2020
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization P. Subramani Nicholas Vadivelu Gautam Kamath 101 83 0 18 Oct 2020
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning Vasisht Duddu A. Boutet Virat Shejwalkar GNN 57 4 0 02 Oct 2020
Quantifying Privacy Leakage in Graph Embedding Vasisht Duddu A. Boutet Virat Shejwalkar MIACV 100 126 0 02 Oct 2020
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models Samuel Gehman Suchin Gururangan Maarten Sap Yejin Choi Noah A. Smith 237 1,226 0 24 Sep 2020
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning S. Kadhe Nived Rajaraman O. O. Koyluoglu Kannan Ramchandran FedML 110 165 0 23 Sep 2020
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images Ming Y. Lu Dehan Kong Jana Lipkova Richard J. Chen Rajendra Singh Drew F. K. Williamson Tiffany Y. Chen Faisal Mahmood FedML MedIm 120 181 0 21 Sep 2020
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding Sahar Abdelnabi Mario Fritz WaLM 86 152 0 07 Sep 2020
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model S. Hahn Junghye Lee FedML 25 2 0 29 Aug 2020
Privacy-preserving Voice Analysis via Disentangled Representations Ranya Aloufi Hamed Haddadi David E. Boyle DRL 130 58 0 29 Jul 2020
Label-Only Membership Inference Attacks Christopher A. Choquette-Choo Florian Tramèr Nicholas Carlini Nicolas Papernot MIACV MIALM 115 519 0 28 Jul 2020
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 224 179 0 28 Jul 2020
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning S. K. Murakonda Reza Shokri 53 78 0 18 Jul 2020
A Survey of Privacy Attacks in Machine Learning M. Rigaki Sebastian Garcia PILM AAML 117 226 0 15 Jul 2020
The Trade-Offs of Private Prediction Laurens van der Maaten Awni Y. Hannun 103 24 0 09 Jul 2020
Green Lighting ML: Confidentiality, Integrity, and Availability of Machine Learning Systems in Deployment Abhishek Gupta Erick Galinkin 29 3 0 09 Jul 2020
Private Speech Classification with Secure Multiparty Computation Kyle Bittner Martine De Cock Rafael Dowsley 70 1 0 01 Jul 2020
Best-Effort Adversarial Approximation of Black-Box Malware Classifiers A. Ali Birhanu Eshete AAML 34 6 0 28 Jun 2020
Free-rider Attacks on Model Aggregation in Federated Learning Yann Fraboni Richard Vidal Marco Lorenzi FedML 85 131 0 21 Jun 2020
Flexible Dataset Distillation: Learn Labels Instead of Images Ondrej Bohdal Yongxin Yang Timothy M. Hospedales DD 99 110 0 15 Jun 2020
Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski Jonathan R. Ullman Alina Oprea FedML 78 251 0 13 Jun 2020
Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges M. Eskénazi Tiancheng Zhao LLMAG AI4TS AI4CE 90 9 0 10 Jun 2020
On the Difficulty of Membership Inference Attacks Shahbaz Rezaei Xin Liu MIACV 46 13 0 27 May 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 190 27 0 27 Apr 2020
Have you forgotten? A method to assess if machine learning models have forgotten data Xiao Liu Sotirios A. Tsaftaris FedML OOD MU 61 27 0 21 Apr 2020
Information Leakage in Embedding Models Congzheng Song A. Raghunathan MIACV 94 274 0 31 Mar 2020
Learn to Forget: Machine Unlearning via Neuron Masking Yang Liu Zhuo Ma Ximeng Liu Jian Liu Zhongyuan Jiang Jianfeng Ma Philip Yu K. Ren MU 88 69 0 24 Mar 2020