Machine Learning Models that Remember Too Much

22 September 2017

Papers citing "Machine Learning Models that Remember Too Much"

50 / 217 papers shown

Title
SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark Jun Niu Xiaoyan Zhu Moxuan Zeng Ge Zhang Qingyang Zhao ... Peng Liu Yulong Shen Xiaohong Jiang Jianfeng Ma Yuqing Zhang 47 3 0 12 Jul 2023
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off Huiqiang Chen Tianqing Zhu Tao Zhang Wanlei Zhou Philip S. Yu FedML 29 43 0 25 Jun 2023
Locally Differentially Private Distributed Online Learning with Guaranteed Optimality Ziqin Chen Yongqiang Wang 39 4 0 25 Jun 2023
Decision-based iterative fragile watermarking for model integrity verification Z. Yin Heng Yin Hang Su Xinpeng Zhang Zhenzhe Gao AAML 28 3 0 13 May 2023
Over-the-Air Federated Averaging with Limited Power and Privacy Budgets Na Yan Kezhi Wang Cunhua Pan K. K. Chai Feng Shu Jiangzhou Wang FedML 38 2 0 05 May 2023
Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence Haoran Li Mingshi Xu Yangqiu Song 95 46 0 04 May 2023
Earning Extra Performance from Restrictive Feedbacks Jing Li Yuangang Pan Yueming Lyu Yinghua Yao Yulei Sui Ivor W. Tsang 25 3 0 28 Apr 2023
Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks Isabell Lederer Rudolf Mayer Andreas Rauber 29 19 0 22 Apr 2023
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation Julio Silva-Rodríguez Jose Dolz Ismail Ben Ayed 66 13 0 29 Mar 2023
Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection Lianyu Wang Meng Wang Daoqiang Zhang Huazhu Fu 26 18 0 20 Mar 2023
A Comparison of Methods for Neural Network Aggregation John Pomerat Aviv Segev OOD FedML 29 0 0 06 Mar 2023
AutoML in The Wild: Obstacles, Workarounds, and Expectations Yuan Sun Qiurong Song Xinning Gui Fenglong Ma Ting Wang 21 13 0 21 Feb 2023
Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare Juexiao Zhou Haoyang Li Xingyu Liao Bin Zhang Wenjia He Zhongxiao Li Longxi Zhou Xin Gao MU 33 13 0 20 Feb 2023
Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy Yusuke Kawamoto Kazumasa Miyake K. Konishi Y. Oiwa 24 4 0 18 Jan 2023
Backdoor Attacks Against Dataset Distillation Yugeng Liu Zheng Li Michael Backes Yun Shen Yang Zhang DD 42 28 0 03 Jan 2023
GAN-based Domain Inference Attack Yuechun Gu Keke Chen 15 11 0 22 Dec 2022
Membership Inference Attacks Against Latent Factor Model Dazhi Hu AAML 30 1 0 15 Dec 2022
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy Ergute Bao Yizheng Zhu X. Xiao Yifan Yang Beng Chin Ooi B. Tan Khin Mi Mi Aung FedML 31 19 0 08 Dec 2022
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise Jie Fu Zhili Chen Xiao Han FedML 25 28 0 29 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 27 0 0 14 Nov 2022
Unbiased Supervised Contrastive Learning C. Barbano Benoit Dufumier Enzo Tartaglione Marco Grangetto Pietro Gori FaML SSL 17 27 0 10 Nov 2022
M-to-N Backdoor Paradigm: A Multi-Trigger and Multi-Target Attack to Deep Learning Models Linshan Hou Zhongyun Hua Yuhong Li Yifeng Zheng Leo Yu Zhang AAML 29 2 0 03 Nov 2022
Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises Patrick J. Tinsley A. Czajka Patrick Flynn GAN 32 8 0 03 Nov 2022
DICTION:DynamIC robusT whIte bOx watermarkiNg scheme for deep neural networks Reda Bellafqira Gouenou Coatrieux 24 5 0 27 Oct 2022
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano Chuan Guo Alexandre Sablayrolles Maziar Sanjabi FedML 29 17 0 24 Oct 2022
Federated Learning and Meta Learning: Approaches, Applications, and Directions Xiaonan Liu Yansha Deng Arumugam Nallanathan M. Bennis 64 32 0 24 Oct 2022
Unsupervised Non-transferable Text Classification Guangtao Zeng Wei Lu 35 6 0 23 Oct 2022
Synthetic Dataset Generation for Privacy-Preserving Machine Learning Efstathia Soufleri Gobinda Saha Kaushik Roy DD 19 2 0 06 Oct 2022
Information Removal at the bottleneck in Deep Neural Networks Enzo Tartaglione 51 2 0 30 Sep 2022
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions Chulin Xie Zhong Cao Yunhui Long Diange Yang Ding Zhao Bo-wen Li 19 4 0 08 Sep 2022
Data Provenance via Differential Auditing Xin Mu Ming Pang Feida Zhu 16 1 0 04 Sep 2022
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction Julien Ferry Ulrich Aïvodji Sébastien Gambs Marie-José Huguet Mohamed Siala AAML 37 14 0 02 Sep 2022
Data Isotopes for Data Provenance in DNNs Emily Wenger Xiuyu Li Ben Y. Zhao Vitaly Shmatikov 20 12 0 29 Aug 2022
Auditing Membership Leakages of Multi-Exit Networks Zheng Li Yiyong Liu Xinlei He Ning Yu Michael Backes Yang Zhang AAML 24 32 0 23 Aug 2022
On the Privacy Effect of Data Enhancement via the Lens of Memorization Xiao-Li Li Qiongxiu Li Zhan Hu Xiaolin Hu 35 13 0 17 Aug 2022
Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learning Guangsheng Yu Xu Wang Ping Yu Caijun Sun Wei Ni R. Liu 14 4 0 08 Aug 2022
RelaxLoss: Defending Membership Inference Attacks without Losing Utility Dingfan Chen Ning Yu Mario Fritz 22 40 0 12 Jul 2022
Matryoshka: Stealing Functionality of Private ML Data by Hiding Models in Model Xudong Pan Yifan Yan Sheng Zhang Mi Zhang Min Yang 27 1 0 29 Jun 2022
Federated Multi-organ Segmentation with Inconsistent Labels Xuanang Xu H. Deng J. Gateno Pingkun Yan FedML 44 22 0 14 Jun 2022
Membership Inference via Backdooring Hongsheng Hu Z. Salcic Gillian Dobbie Jinjun Chen Lichao Sun Xuyun Zhang MIACV 36 30 0 10 Jun 2022
Lessons Learned: Defending Against Property Inference Attacks Joshua Stock Jens Wettlaufer Daniel Demmler Hannes Federrath AAML 41 1 0 18 May 2022
Byzantine Fault Tolerance in Distributed Machine Learning : a Survey Djamila Bouhata Hamouma Moumen Moumen Hamouma Ahcène Bounceur AI4CE 27 7 0 05 May 2022
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation? Héber H. Arcolezi Jean-François Couchot Denis Renaud Bechara al Bouna X. Xiao AI4TS 36 5 0 01 May 2022
Unsupervised Learning of Unbiased Visual Representations C. Barbano Enzo Tartaglione Marco Grangetto SSL CML OOD 35 1 0 26 Apr 2022
You Are What You Write: Preserving Privacy in the Era of Large Language Models Richard Plant V. Giuffrida Dimitra Gkatzia PILM 38 19 0 20 Apr 2022
Finding MNEMON: Reviving Memories of Node Embeddings Yun Shen Yufei Han Zhikun Zhang Min Chen Tingyue Yu Michael Backes Yang Zhang Gianluca Stringhini 21 14 0 14 Apr 2022
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets Florian Tramèr Reza Shokri Ayrton San Joaquin Hoang Minh Le Matthew Jagielski Sanghyun Hong Nicholas Carlini MIACV 38 107 0 31 Mar 2022
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning Georg Pichler Marco Romanelli L. Rey Vega Pablo Piantanida FedML 36 10 0 30 Mar 2022
Training a Tokenizer for Free with Private Federated Learning Eugene Bagdasaryan Congzheng Song Rogier van Dalen M. Seigel Áine Cahill FedML 22 5 0 15 Mar 2022
Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes C. Li Andrea Cavallaro PICV 14 0 0 05 Mar 2022