Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

18 October 2016

Papers citing "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data"

50 / 206 papers shown

Title
Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation Qianren Mao Qili Zhang Hanwen Hao Zhentao Han Runhua Xu ... Jing Chen Yangqiu Song Jin Dong Jianxin Li Philip S. Yu 76 1 0 27 Apr 2025
Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology Yanjie Wu Yuhao Ji Saiho Lee Juniad Akram Ali Braytee Ali Anaissi 39 0 0 23 Apr 2025
A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Yihan Lin Zhirong Bella Yu Simon Lee SyDa 51 0 0 20 Apr 2025
DP-GPL: Differentially Private Graph Prompt Learning Jing Xu Franziska Boenisch Iyiola Emmanuel Olatunji Adam Dziedzic AAML 58 0 0 13 Mar 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 90 0 0 02 Dec 2024
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning Fengyu Gao Ruida Zhou T. Wang Cong Shen Jing Yang 41 2 0 15 Oct 2024
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 39 0 0 03 Oct 2024
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation Bochao Liu Jianghu Lu Pengju Wang Junjie Zhang Dan Zeng Zhenxing Qian Shiming Ge 25 1 0 19 Sep 2024
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography Pingzhi Li Tianlong Chen Junyu Liu FedML 28 1 0 06 Sep 2024
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation Shiming Ge Bochao Liu Pengju Wang Yong Li Dan Zeng FedML 44 9 0 04 Sep 2024
Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification Baris Büyüktas Kenneth Weitzel Sebastian Völkers Felix Zailskas Begüm Demir 54 6 0 24 May 2024
PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy Zepeng Jiang Weiwei Ni Yifan Zhang PICV 21 1 0 19 Apr 2024
Privacy at a Price: Exploring its Dual Impact on AI Fairness Mengmeng Yang Ming Ding Youyang Qu Wei Ni David B. Smith Thierry Rakotoarivelo 30 1 0 15 Apr 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 30 1 0 06 Mar 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey Chaoyu Zhang Shaoyu Li AILaw 60 3 0 25 Feb 2024
On the Byzantine-Resilience of Distillation-Based Federated Learning Christophe Roux Max Zimmer Sebastian Pokutta AAML 57 1 0 19 Feb 2024
Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures Yonghao Yu Shunan Zhu Jinglu Hu AAML FedML 35 0 0 31 Jan 2024
Hot PATE: Private Aggregation of Distributions for Diverse Task Edith Cohen Benjamin Cohen-Wang Xin Lyu Jelani Nelson Tamas Sarlos Uri Stemmer 52 3 0 04 Dec 2023
Preserving Node-level Privacy in Graph Neural Networks Zihang Xiang Tianhao Wang Di Wang 27 6 0 12 Nov 2023
Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble Zhe Liu Ozlem Kalinli MU KELM 28 2 0 28 Sep 2023
A Survey of Graph Unlearning Anwar Said Tyler Derr Mudassir Shabbir W. Abbas X. Koutsoukos MU 28 7 0 23 Aug 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 42 11 0 11 Aug 2023
Teacher-Student Architecture for Knowledge Distillation: A Survey Chengming Hu Xuan Li Danyang Liu Haolun Wu Xi Chen Ju Wang Xue Liu 21 16 0 08 Aug 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 43 23 0 20 Jul 2023
DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation Rodrigo Castellon Achintya Gopal Brian Bloniarz David S. Rosenberg 26 7 0 19 Jul 2023
Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection Eli Chien Wei-Ning Chen Chao Pan Pan Li Ayfer Özgür O. Milenkovic 36 12 0 12 Jul 2023
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning Tamas Madl Weijie Xu Olivia Choudhury Matthew Howard 37 5 0 04 Jul 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 52 11 0 06 Jun 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 38 19 0 23 May 2023
Differentially Private Adapters for Parameter Efficient Acoustic Modeling Chun-Wei Ho Chao-Han Huck Yang Sabato Marco Siniscalchi 26 1 0 19 May 2023
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase Chih-Hsun Lin Chia-Yi Hsu Chia-Mu Yu Yang Cao Chun-ying Huang 36 1 0 20 Apr 2023
Federated Learning without Full Labels: A Survey Yilun Jin Yang Liu Kai Chen Qian Yang FedML 17 26 0 25 Mar 2023
PRIMO: Private Regression in Multiple Outcomes Seth Neel 25 0 0 07 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 96 167 0 01 Mar 2023
Graph-based Knowledge Distillation: A survey and experimental evaluation Jing Liu Tongya Zheng Guanzheng Zhang Qinfen Hao 33 8 0 27 Feb 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning Van Tuan Tran Huy Hieu Pham Kok-Seng Wong FedML 39 7 0 22 Feb 2023
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility Mohammad Yaghini Patty Liu Franziska Boenisch Nicolas Papernot FedML FaML 41 8 0 17 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 38 14 0 06 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 35 18 0 22 Jan 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping Jiyan He Xuechen Li Da Yu Huishuai Zhang Janardhan Kulkarni Y. Lee A. Backurs Nenghai Yu Jiang Bian 30 46 0 03 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
On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation Yucong Liu ChiHua Wang Guang Cheng 29 7 0 28 Nov 2022
Private Multi-Winner Voting for Machine Learning Adam Dziedzic Christopher A. Choquette-Choo Natalie Dullerud Vinith Suriyakumar Ali Shahin Shamsabadi Muhammad Ahmad Kaleem S. Jha Nicolas Papernot Xiao Wang 42 1 0 23 Nov 2022
Private Ad Modeling with DP-SGD Carson E. Denison Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Krishnagiri Narra Amer Sinha A. Varadarajan Chiyuan Zhang 32 14 0 21 Nov 2022
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder Bjarne Pfitzner B. Arnrich FedML 33 19 0 21 Nov 2022
A Survey on Differential Privacy with Machine Learning and Future Outlook Samah Baraheem Z. Yao SyDa 21 1 0 19 Nov 2022
Private Set Generation with Discriminative Information Dingfan Chen Raouf Kerkouche Mario Fritz DD 30 34 0 07 Nov 2022
Revisiting Hyperparameter Tuning with Differential Privacy Youlong Ding Xueyang Wu 24 0 0 03 Nov 2022
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels Qiuchen Zhang Jing Ma Jian Lou Li Xiong Xiaoqian Jiang NoLa 21 0 0 03 Nov 2022
Teacher-Student Architecture for Knowledge Learning: A Survey Chengming Hu Xuan Li Dan Liu Xi Chen Ju Wang Xue Liu 25 35 0 28 Oct 2022