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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
SoK: Differential Privacy on Graph-Structured Data Tamara T. Mueller Dmitrii Usynin Johannes C. Paetzold Daniel Rueckert Georgios Kaissis 88 15 0 17 Mar 2022
Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach W. Boulila Adel Ammar Bilel Benjdira Anis Koubaa 61 13 0 15 Mar 2022
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources Ghadeer O. Ghosheh Jin Li T. Zhu 97 42 0 14 Mar 2022
HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data Fumiyuki Kato Tsubasa Takahashi Shun Takagi Yang Cao Seng Pei Liew Masatoshi Yoshikawa 55 6 0 14 Mar 2022
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy Dayong Ye Sheng Shen Tianqing Zhu B. Liu Wanlei Zhou MIACV 68 66 0 13 Mar 2022
Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms Armando Angrisani Mina Doosti E. Kashefi 87 12 0 07 Mar 2022
Differentially Private Label Protection in Split Learning Xin Yang Jiankai Sun Yuanshun Yao Junyuan Xie Chong-Jun Wang FedML 114 36 0 04 Mar 2022
Faking feature importance: A cautionary tale on the use of differentially-private synthetic data Oscar Giles Kasra Hosseini Grigorios Mingas Oliver Strickson Louise A. Bowler ... A. Heppenstall N. Lomax N. Malleson Martin O'Reilly Sebastian Vollmerteke 98 8 0 02 Mar 2022
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation Sina Sajadmanesh Ali Shahin Shamsabadi A. Bellet D. Gática-Pérez 85 67 0 02 Mar 2022
Does Label Differential Privacy Prevent Label Inference Attacks? Ruihan Wu Jinfu Zhou Kilian Q. Weinberger Chuan Guo 59 16 0 25 Feb 2022
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees Franziska Boenisch Christopher Muhl Roy Rinberg Jannis Ihrig Adam Dziedzic 79 18 0 21 Feb 2022
PPA: Preference Profiling Attack Against Federated Learning Chunyi Zhou Yansong Gao Anmin Fu Kai Chen Zhiyang Dai Zhi-Li Zhang Minhui Xue Yuqing Zhang AAML 67 23 0 10 Feb 2022
Locally Differentially Private Distributed Deep Learning via Knowledge Distillation Di Zhuang Mingchen Li Jerome Chang FedML 36 2 0 07 Feb 2022
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation Wenxiao Wang Alexander Levine Soheil Feizi AAML 89 60 0 05 Feb 2022
Differentially Private Graph Classification with GNNs Tamara T. Mueller Johannes C. Paetzold Chinmay Prabhakar Dmitrii Usynin Daniel Rueckert Georgios Kaissis 95 22 0 05 Feb 2022
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge Eustache Diemert Romain Fabre Alexandre Gilotte Fei Jia Basile Leparmentier Jérémie Mary Zhonghua Qu Ugo Tanielian Hui Yang 80 6 0 31 Jan 2022
Syfer: Neural Obfuscation for Private Data Release Adam Yala Victor Quach H. Esfahanizadeh Rafael G. L. DÓliveira K. Duffy Muriel Médard Tommi Jaakkola Regina Barzilay PICV 130 7 0 28 Jan 2022
Increasing the Cost of Model Extraction with Calibrated Proof of Work Adam Dziedzic Muhammad Ahmad Kaleem Y. Lu Nicolas Papernot FedML MIACV AAML MLAU 130 29 0 23 Jan 2022
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges Nuria Rodríguez-Barroso Daniel Jiménez López M. V. Luzón Francisco Herrera Eugenio Martínez-Cámara FedML 85 229 0 20 Jan 2022
Submix: Practical Private Prediction for Large-Scale Language Models Antonio A. Ginart Laurens van der Maaten James Zou Chuan Guo 87 23 0 04 Jan 2022
Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models Made Nindyatama Nityasya Haryo Akbarianto Wibowo Rendi Chevi Radityo Eko Prasojo Alham Fikri Aji 80 6 0 03 Jan 2022
Financial Vision Based Differential Privacy Applications Jun-Hao Chen Yi-Jen Wang Yun-Cheng Tsai Samuel Yen-Chi Chen FedML 43 1 0 28 Dec 2021
DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning Ismat Jarin Birhanu Eshete AAML 69 10 0 24 Dec 2021
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 110 188 0 06 Dec 2021
Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Indra Ramaswamy Shuang Song Thomas Steinke Vinith Suriyakumar Om Thakkar Abhradeep Thakurta 96 51 0 01 Dec 2021
Node-Level Differentially Private Graph Neural Networks Ameya Daigavane Gagan Madan Aditya Sinha Abhradeep Thakurta Gaurav Aggarwal Prateek Jain 80 59 0 23 Nov 2021
Decentralized Federated Learning through Proxy Model Sharing Shivam Kalra Junfeng Wen Jesse C. Cresswell M. Volkovs Hamid R. Tizhoosh FedML 99 101 0 22 Nov 2021
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence Tianshi Cao Alex Bie Arash Vahdat Sanja Fidler Karsten Kreis SyDa DiffM 89 72 0 01 Nov 2021
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning Yansong Gao Qun Li Yifeng Zheng Guohong Wang Jiannan Wei Mang Su 83 3 0 26 Oct 2021
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture Xinyu Tang Saeed Mahloujifar Liwei Song Virat Shejwalkar Milad Nasr Amir Houmansadr Prateek Mittal 69 80 0 15 Oct 2021
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy L. Du Zhikun Zhang Shaojie Bai Changchang Liu S. Ji Peng Cheng Jiming Chen 142 38 0 14 Oct 2021
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 262 373 0 13 Oct 2021
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data Georgi Ganev Bristena Oprisanu Emiliano De Cristofaro 139 58 0 23 Sep 2021
Releasing Graph Neural Networks with Differential Privacy Guarantees Iyiola E. Olatunji Thorben Funke Megha Khosla 118 47 0 18 Sep 2021
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 88 106 0 10 Aug 2021
DarkGAN: Exploiting Knowledge Distillation for Comprehensible Audio Synthesis with GANs J. Nistal Stefan Lattner G. Richard 78 9 0 03 Aug 2021
Generative Models for Security: Attacks, Defenses, and Opportunities L. A. Bauer Vincent Bindschaedler 110 4 0 21 Jul 2021
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging Richard Osuala Kaisar Kushibar Lidia Garrucho Akis Linardos Zuzanna Szafranowska Stefan Klein Ben Glocker Oliver Díaz Karim Lekadir MedIm 104 45 0 20 Jul 2021
Private Graph Data Release: A Survey Yang D. Li M. Purcell Thierry Rakotoarivelo David B. Smith Thilina Ranbaduge K. S. Ng 106 26 0 09 Jul 2021
DTGAN: Differential Private Training for Tabular GANs A. Kunar Robert Birke Zilong Zhao L. Chen 66 11 0 06 Jul 2021
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 113 76 0 04 Jul 2021
Large Scale Private Learning via Low-rank Reparametrization Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu 87 106 0 17 Jun 2021
An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises Mayana Pereira Meghana Kshirsagar Soumendu Sundar Mukherjee Rahul Dodhia J. L. Ferres 95 9 0 15 Jun 2021
Hermite Polynomial Features for Private Data Generation Margarita Vinaroz Mohammad-Amin Charusaie Frederik Harder Kamil Adamczewski Mijung Park 111 25 0 09 Jun 2021
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning Seng Pei Liew Tsubasa Takahashi Michihiko Ueno FedML 76 29 0 08 Jun 2021
Antipodes of Label Differential Privacy: PATE and ALIBI Mani Malek Ilya Mironov Karthik Prasad I. Shilov Florian Tramèr 71 66 0 07 Jun 2021
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI Mohit Kumar Bernhard A. Moser Lukas Fischer B. Freudenthaler 89 1 0 06 Jun 2021
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs Mohammad Malekzadeh Anastasia Borovykh Deniz Gündüz MIACV 87 42 0 25 May 2021
Differentially Private Federated Knowledge Graphs Embedding Hao Peng Haoran Li Yangqiu Song V. Zheng Jianxin Li FedML 90 85 0 17 May 2021
Differentially Private Transferrable Deep Learning with Membership-Mappings Mohit Kumar 61 9 0 10 May 2021