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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 / 353 papers shown

Title
The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing Meenatchi Sundaram Muthu Selva Annamalai Borja Balle Jamie Hayes Georgios Kaissis Emiliano De Cristofaro 35 0 0 20 Jun 2025
Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning Roy Rinberg Ilia Shumailov Vikrant Singhal Rachel Cummings Nicolas Papernot 28 0 0 14 Jun 2025
Privacy Amplification Through Synthetic Data: Insights from Linear Regression Clément Pierquin A. Bellet Marc Tommasi Matthieu Boussard MIACV 117 0 0 05 Jun 2025
Differential Privacy for Deep Learning in Medicine Marziyeh Mohammadi Mohsen Vejdanihemmat Mahshad Lotfinia M. Rusu Daniel Truhn Andreas K. Maier Soroosh Tayebi Arasteh 49 0 0 31 May 2025
Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data Andre Catarino Rui Melo Rui Abreu Luís Cruz DiffM 39 0 0 20 May 2025
A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Yihan Lin Zhirong Bella Yu Simon Lee SyDa 146 1 0 20 Apr 2025
DP-GPL: Differentially Private Graph Prompt Learning Jing Xu Franziska Boenisch Iyiola Emmanuel Olatunji Adam Dziedzic AAML 111 0 0 13 Mar 2025
Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond Qiongxiu Li Xiaoyu Luo Yiyi Chen Johannes Bjerva 242 2 0 10 Mar 2025
SoK: What Makes Private Learning Unfair? Kai Yao Marc Juarez 78 0 0 24 Jan 2025
Advancing privacy in learning analytics using differential privacy Qinyi Liu Ronas Shakya Mohammad Khalil Jelena Jovanovic 83 2 0 03 Jan 2025
Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models Martin Pawelczyk Lillian Sun Zhenting Qi Aounon Kumar Himabindu Lakkaraju 162 3 0 03 Jan 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 189 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 95 3 0 15 Oct 2024
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 125 0 0 03 Oct 2024
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography Pingzhi Li Tianlong Chen Junyu Liu FedML 73 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 102 11 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 95 6 0 24 May 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 79 2 0 06 Mar 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey Chaoyu Zhang Shaoyu Li AILaw 135 4 0 25 Feb 2024
On the Byzantine-Resilience of Distillation-Based Federated Learning Christophe Roux Max Zimmer Sebastian Pokutta AAML 150 1 0 19 Feb 2024
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning Yousef Alsenani Rahul Mishra Khaled R. Ahmed Atta Ur Rahman FedML 102 2 0 14 Feb 2024
Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning William Watkins Heehwan Wang Sang-Peel Bae Huan-Hsin Tseng Jiook Cha Samuel Yen-Chi Chen Shinjae Yoo 29 3 0 15 Jan 2024
Hot PATE: Private Aggregation of Distributions for Diverse Task Edith Cohen Benjamin Cohen-Wang Xin Lyu Jelani Nelson Tamas Sarlos Uri Stemmer 119 4 0 04 Dec 2023
Preserving Node-level Privacy in Graph Neural Networks Zihang Xiang Tianhao Wang Di Wang 83 12 0 12 Nov 2023
Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble Zhe Liu Ozlem Kalinli MU KELM 90 2 0 28 Sep 2023
Generating tabular datasets under differential privacy G. Truda DiffM 61 6 0 28 Aug 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 86 13 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 94 19 0 08 Aug 2023
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering Ce Feng Nuo Xu Wujie Wen Parv Venkitasubramaniam Caiwen Ding 63 4 0 25 Jul 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 109 28 0 20 Jul 2023
When Synthetic Data Met Regulation Georgi Ganev 84 2 0 01 Jul 2023
FFPDG: Fast, Fair and Private Data Generation Weijie Xu Jinjin Zhao Francis Iannacci Bo Wang 83 12 0 30 Jun 2023
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities Xudong Shen H. Brown Jiashu Tao Martin Strobel Yao Tong Akshay Narayan Harold Soh Finale Doshi-Velez 105 3 0 22 Jun 2023
An information-Theoretic Approach to Semi-supervised Transfer Learning Daniel Jakubovitz David Uliel Miguel R. D. Rodrigues Raja Giryes 64 1 0 11 Jun 2023
Confidential Truth Finding with Multi-Party Computation (Extended Version) Angelo Saadeh Pierre Senellart S. Bressan HILM FedML 56 1 0 24 May 2023
Differentially Private Adapters for Parameter Efficient Acoustic Modeling Chun-Wei Ho Chao-Han Huck Yang Sabato Marco Siniscalchi 100 1 0 19 May 2023
DPMLBench: Holistic Evaluation of Differentially Private Machine Learning Chengkun Wei Ming-Hui Zhao Zhikun Zhang Min Chen Wenlong Meng Bodong Liu Yuan-shuo Fan Wenzhi Chen 96 11 0 10 May 2023
FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation Sheng Wan Dashan Gao Hanlin Gu Daning Hu FedML 65 7 0 09 May 2023
Practical Differentially Private and Byzantine-resilient Federated Learning Zihang Xiang Tianhao Wang Wanyu Lin Di Wang FedML 75 23 0 15 Apr 2023
When approximate design for fast homomorphic computation provides differential privacy guarantees Arnaud Grivet Sébert Martin Zuber Oana Stan Renaud Sirdey Cédric Gouy-Pailler TPM 55 1 0 06 Apr 2023
PRIMO: Private Regression in Multiple Outcomes Seth Neel 85 0 0 07 Mar 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning Van Tuan Tran Huy Hieu Pham Kok-Seng Wong FedML 98 8 0 22 Feb 2023
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations Mimee Xu Jiankai Sun Xin Yang K. Yao Chong-Jun Wang MU CML CLL 52 13 0 13 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 106 16 0 06 Feb 2023
Practical Differentially Private Hyperparameter Tuning with Subsampling A. Koskela Tejas D. Kulkarni 114 18 0 27 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 98 19 0 22 Jan 2023
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy Rachel Redberg Yuqing Zhu Yu Wang 93 7 0 31 Dec 2022
PreFair: Privately Generating Justifiably Fair Synthetic Data David Pujol Amir Gilad Ashwin Machanavajjhala 79 7 0 20 Dec 2022
Regression with Label Differential Privacy Badih Ghazi Pritish Kamath Ravi Kumar Ethan Leeman Pasin Manurangsi A. Varadarajan Chiyuan Zhang 95 17 0 12 Dec 2022
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 122 49 0 03 Dec 2022