Machine Learning Models that Remember Too Much

22 September 2017

Papers citing "Machine Learning Models that Remember Too Much"

50 / 217 papers shown

Title
A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis Teng Wang Xuefeng Zhang Xuefeng Zhang Xinyu Yang 14 86 0 11 Oct 2020
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning Vasisht Duddu A. Boutet Virat Shejwalkar GNN 21 4 0 02 Oct 2020
Quantifying Privacy Leakage in Graph Embedding Vasisht Duddu A. Boutet Virat Shejwalkar MIACV 17 119 0 02 Oct 2020
A Systematic Review on Model Watermarking for Neural Networks Franziska Boenisch AAML 11 64 0 25 Sep 2020
An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks Sumit Kumar Jha Susmit Jha Rickard Ewetz Sunny Raj Alvaro Velasquez L. Pullum A. Swami MIACV 8 8 0 17 Sep 2020
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning Mohammad Naseri Jamie Hayes Emiliano De Cristofaro FedML 33 144 0 08 Sep 2020
Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries Shadi Rahimian Tribhuvanesh Orekondy Mario Fritz MIACV 11 25 0 01 Sep 2020
A non-discriminatory approach to ethical deep learning Enzo Tartaglione Marco Grangetto 21 3 0 04 Aug 2020
Privacy-preserving Voice Analysis via Disentangled Representations Ranya Aloufi Hamed Haddadi David E. Boyle DRL 26 58 0 29 Jul 2020
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning S. K. Murakonda Reza Shokri 6 73 0 18 Jul 2020
Less is More: A privacy-respecting Android malware classifier using Federated Learning Rafa Gálvez Veelasha Moonsamy Claudia Díaz FedML 6 30 0 16 Jul 2020
Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent Lauren Watson Benedek Rozemberczki Rik Sarkar 16 0 0 25 Jun 2020
Large image datasets: A pyrrhic win for computer vision? Vinay Uday Prabhu Abeba Birhane 28 358 0 24 Jun 2020
On the Difficulty of Membership Inference Attacks Shahbaz Rezaei Xin Liu MIACV 22 13 0 27 May 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 27 83 0 18 May 2020
When Machine Unlearning Jeopardizes Privacy Min Chen Zhikun Zhang Tianhao Wang Michael Backes Mathias Humbert Yang Zhang MIACV 31 218 0 05 May 2020
Enhancing Privacy via Hierarchical Federated Learning A. Wainakh Alejandro Sánchez Guinea Tim Grube M. Mühlhäuser FedML 28 45 0 23 Apr 2020
Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks Di Gao Cheng Zhuo 6 4 0 05 Apr 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 359 0 24 Mar 2020
Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations Aditya Golatkar Alessandro Achille Stefano Soatto MU OOD 22 189 0 05 Mar 2020
Formalizing Data Deletion in the Context of the Right to be Forgotten Sanjam Garg S. Goldwasser Prashant Nalini Vasudevan AILaw MU 43 82 0 25 Feb 2020
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models Shawn Shan Emily Wenger Jiayun Zhang Huiying Li Haitao Zheng Ben Y. Zhao PICV MU 27 24 0 19 Feb 2020
The Differentially Private Lottery Ticket Mechanism Lovedeep Gondara Ke Wang Ricardo Silva Carvalho 6 3 0 16 Feb 2020
Salvaging Federated Learning by Local Adaptation Tao Yu Eugene Bagdasaryan Vitaly Shmatikov FedML 25 260 0 12 Feb 2020
Towards Security Threats of Deep Learning Systems: A Survey Yingzhe He Guozhu Meng Kai Chen Xingbo Hu Jinwen He AAML ELM 15 14 0 28 Nov 2019
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy Min Du R. Jia D. Song AAML 27 175 0 16 Nov 2019
CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations Qiao Zhang Cong Wang Chunsheng Xin Hongyi Wu 13 4 0 12 Nov 2019
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks Aditya Golatkar Alessandro Achille Stefano Soatto CLL MU 11 471 0 12 Nov 2019
Theoretical Guarantees for Model Auditing with Finite Adversaries Mario Díaz Peter Kairouz Jiachun Liao Lalitha Sankar MLAU AAML 34 2 0 08 Nov 2019
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning Frederik Harder Jonas Köhler Max Welling Mijung Park 15 0 0 15 Oct 2019
Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning Congzheng Song Reza Shokri MIACV 16 5 0 27 Sep 2019
That which we call private Ulfar Erlingsson Ilya Mironov A. Raghunathan Shuang Song 13 26 0 08 Aug 2019
Local Differential Privacy for Deep Learning Pathum Chamikara Mahawaga Arachchige P. Bertók I. Khalil Dongxi Liu S. Çamtepe Mohammed Atiquzzaman 41 220 0 08 Aug 2019
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn) S. Lugan P. Desbordes Luis Xavier Ramos Tormo Axel Legay Benoit Macq FedML 20 31 0 18 Jun 2019
Membership Privacy for Machine Learning Models Through Knowledge Transfer Virat Shejwalkar Amir Houmansadr 22 10 0 15 Jun 2019
Effectiveness of Distillation Attack and Countermeasure on Neural Network Watermarking Ziqi Yang Hung Dang E. Chang AAML 27 34 0 14 Jun 2019
Interpretable and Differentially Private Predictions Frederik Harder Matthias Bauer Mijung Park FAtt 14 52 0 05 Jun 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 6 235 0 24 May 2019
Real numbers, data science and chaos: How to fit any dataset with a single parameter L. Boué 6 2 0 28 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 69 26 0 24 Apr 2019
Differentially Private Model Publishing for Deep Learning Lei Yu Ling Liu C. Pu Mehmet Emre Gursoy Stacey Truex FedML 15 264 0 03 Apr 2019
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning A. Salem Apratim Bhattacharyya Michael Backes Mario Fritz Yang Zhang FedML AAML MIACV 17 250 0 01 Apr 2019
STYLE-ANALYZER: fixing code style inconsistencies with interpretable unsupervised algorithms Vadim Markovtsev Waren Long Hugo Mougard Konstantin Slavnov Egor Bulychev 14 20 0 01 Apr 2019
Copying Machine Learning Classifiers Irene Unceta Jordi Nin O. Pujol 14 18 0 05 Mar 2019
TamperNN: Efficient Tampering Detection of Deployed Neural Nets Erwan Le Merrer Gilles Tredan MLAU AAML 6 9 0 01 Mar 2019
Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment Ziqi Yang E. Chang Zhenkai Liang MLAU 33 60 0 22 Feb 2019
Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes O. Ohrimenko AAML FedML 19 74 0 08 Jan 2019
Reaching Data Confidentiality and Model Accountability on the CalTrain Zhongshu Gu Hani Jamjoom D. Su Heqing Huang Jialong Zhang Tengfei Ma Dimitrios E. Pendarakis Ian Molloy FedML 13 15 0 07 Dec 2018
Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase Jianfeng Chi Emmanuel Owusu Xuwang Yin Tong Yu William Chan P. Tague Yuan Tian FedML 19 28 0 07 Dec 2018
Differentially Private Fair Learning Matthew Jagielski Michael Kearns Jieming Mao Alina Oprea Aaron Roth Saeed Sharifi-Malvajerdi Jonathan R. Ullman FaML FedML 19 147 0 06 Dec 2018