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 / 710 papers shown

Title
Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski Jonathan R. Ullman Alina Oprea FedML 10 237 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 36 9 0 10 Jun 2020
Trade-offs between membership privacy & adversarially robust learning Jamie Hayes SILM 20 3 0 08 Jun 2020
On the Difficulty of Membership Inference Attacks Shahbaz Rezaei Xin Liu MIACV 6 13 0 27 May 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 62 25 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 11 26 0 21 Apr 2020
Information Leakage in Embedding Models Congzheng Song A. Raghunathan MIACV 16 260 0 31 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 15 61 0 24 Mar 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 358 0 24 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 1,705 0 18 Mar 2020
Cryptanalytic Extraction of Neural Network Models Nicholas Carlini Matthew Jagielski Ilya Mironov FedML MLAU MIACV AAML 70 134 0 10 Mar 2020
Towards Probabilistic Verification of Machine Unlearning David M. Sommer Liwei Song Sameer Wagh Prateek Mittal AAML 11 71 0 09 Mar 2020
Federating Recommendations Using Differentially Private Prototypes Mónica Ribero Jette Henderson Sinead Williamson H. Vikalo FedML 12 39 0 01 Mar 2020
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping Sanghyun Hong Varun Chandrasekaran Yigitcan Kaya Tudor Dumitras Nicolas Papernot AAML 12 136 0 26 Feb 2020
Approximate Data Deletion from Machine Learning Models Zachary Izzo Mary Anne Smart Kamalika Chaudhuri James Y. Zou MU 9 248 0 24 Feb 2020
Differentially Private Set Union Sivakanth Gopi P. Gulhane Janardhan Kulkarni J. Shen Milad Shokouhi Sergey Yekhanin FedML 11 32 0 22 Feb 2020
Data Heterogeneity Differential Privacy: From Theory to Algorithm Yilin Kang Jian Li Yong Liu Weiping Wang 23 1 0 20 Feb 2020
Machine Unlearning: Linear Filtration for Logit-based Classifiers Thomas Baumhauer Pascal Schöttle Matthias Zeppelzauer MU 104 130 0 07 Feb 2020
CryptoSPN: Privacy-preserving Sum-Product Network Inference Amos Treiber Alejandro Molina Christian Weinert T. Schneider Kristian Kersting 11 10 0 03 Feb 2020
Model Extraction Attacks against Recurrent Neural Networks Tatsuya Takemura Naoto Yanai T. Fujiwara MLAU MIACV AAML 15 15 0 01 Feb 2020
Analyzing Information Leakage of Updates to Natural Language Models Santiago Zanella Béguelin Lukas Wutschitz Shruti Tople Victor Rühle Andrew J. Paverd O. Ohrimenko Boris Köpf Marc Brockschmidt ELM MIACV FedML PILM KELM 6 125 0 17 Dec 2019
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 69 6,063 0 10 Dec 2019
Machine Unlearning Lucas Bourtoule Varun Chandrasekaran Christopher A. Choquette-Choo Hengrui Jia Adelin Travers Baiwu Zhang David Lie Nicolas Papernot MU 14 806 0 09 Dec 2019
Security of Deep Learning Methodologies: Challenges and Opportunities Shahbaz Rezaei Xin Liu AAML 26 4 0 08 Dec 2019
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning U. Tantipongpipat Chris Waites Digvijay Boob Amaresh Ankit Siva Rachel Cummings SyDa 16 31 0 06 Dec 2019
PAC learning with stable and private predictions Y. Dagan Vitaly Feldman 12 12 0 24 Nov 2019
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy Min Du R. Jia D. Song AAML 11 175 0 16 Nov 2019
Revocable Federated Learning: A Benchmark of Federated Forest Yang Liu Zhuo Ma Ximeng Liu Zhuzhu Wang Siqi Ma Ken Ren FedML MU 16 10 0 08 Nov 2019
Enhancing the Privacy of Federated Learning with Sketching Zaoxing Liu Tian Li Virginia Smith Vyas Sekar FedML 11 20 0 05 Nov 2019
Fault Tolerance of Neural Networks in Adversarial Settings Vasisht Duddu N. Pillai D. V. Rao V. Balas SILM AAML 11 11 0 30 Oct 2019
Efficient Privacy-Preserving Stochastic Nonconvex Optimization Lingxiao Wang Bargav Jayaraman David E. Evans Quanquan Gu 11 28 0 30 Oct 2019
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition Mimansa Jaiswal E. Provost 25 72 0 29 Oct 2019
Thieves on Sesame Street! Model Extraction of BERT-based APIs Kalpesh Krishna Gaurav Singh Tomar Ankur P. Parikh Nicolas Papernot Mohit Iyyer MIACV MLAU 25 193 0 27 Oct 2019
Weighted Distributed Differential Privacy ERM: Convex and Non-convex Yilin Kang Yong Liu Weiping Wang 13 10 0 23 Oct 2019
Actor Critic with Differentially Private Critic Jonathan Lebensold William L. Hamilton Borja Balle Doina Precup OffRL 15 9 0 14 Oct 2019
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints Felix Sattler K. Müller Wojciech Samek FedML 40 965 0 04 Oct 2019
Alleviating Privacy Attacks via Causal Learning Shruti Tople Amit Sharma A. Nori MIACV OOD 19 32 0 27 Sep 2019
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform Mathias Lécuyer Riley Spahn Kiran Vodrahalli Roxana Geambasu Daniel J. Hsu 9 44 0 04 Sep 2019
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML Ashvin Agrawal Rony Chatterjee Carlo Curino Avrilia Floratou Neha Godwal ... Karla Saur Rathijit Sen Markus Weimer Travis Wright Yiwen Zhu 15 39 0 30 Aug 2019
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference Alexandre Sablayrolles Matthijs Douze Yann Ollivier Cordelia Schmid Hervé Jégou MIACV 9 351 0 29 Aug 2019
Federated Learning: Challenges, Methods, and Future Directions Tian Li Anit Kumar Sahu Ameet Talwalkar Virginia Smith FedML 13 4,413 0 21 Aug 2019
Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges Solmaz Niknam Harpreet S. Dhillon J. H. Reed 17 597 0 30 Jul 2019
The Cost of a Reductions Approach to Private Fair Optimization Daniel Alabi 28 3 0 23 Jun 2019
Membership Privacy for Machine Learning Models Through Knowledge Transfer Virat Shejwalkar Amir Houmansadr 14 10 0 15 Jun 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 21 481 0 12 Jun 2019
ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain Tu Bui Daniel Cooper John Collomosse Mark Bell Alex Green ... Jez Higgins Arindra Das Jared Keller Olivier Thereaux Alan W. Brown 4 20 0 26 Apr 2019
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning Xinlei Pan Weiyao Wang Xiaoshuai Zhang Bo-wen Li Jinfeng Yi D. Song MIACV 61 26 0 24 Apr 2019
Federated Learning Of Out-Of-Vocabulary Words Mingqing Chen Rajiv Mathews Tom Y. Ouyang F. Beaufays FedML 17 162 0 26 Mar 2019
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018
Déjà Vu: an empirical evaluation of the memorization properties of ConvNets Alexandre Sablayrolles Matthijs Douze Cordelia Schmid Hervé Jégou 11 18 0 17 Sep 2018