Differential Privacy Has Disparate Impact on Model Accuracy

28 May 2019

Papers citing "Differential Privacy Has Disparate Impact on Model Accuracy"

50 / 109 papers shown

Title
Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness Héber H. Arcolezi Mina Alishahi Adda-Akram Bendoukha Nesrine Kaaniche 36 0 0 12 May 2025
Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy Rushabh Solanki Meghana Bhange Ulrich Aïvodji Elliot Creager 39 0 0 09 May 2025
Federated learning, ethics, and the double black box problem in medical AI Joshua Hatherley Anders Søgaard Angela Ballantyne Ruben Pauwels FedML 58 0 0 29 Apr 2025
A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks Georgios Papadopoulos Shaltiel Eloul Yash Satsangi Jamie Heredge Niraj Kumar Chun-Fu Chen Marco Pistoia 58 0 0 17 Apr 2025
Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics Daniele Malpetti Marco Scutari Francesco Gualdi Jessica van Setten Sander van der Laan Saskia Haitjema Aaron Mark Lee Isabelle Hering Francesca Mangili FedML AI4CE 109 1 0 12 Mar 2025
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models Ruta Binkyte Ivaxi Sheth Zhijing Jin Mohammad Havaei Bernhard Schölkopf Mario Fritz 218 0 0 28 Feb 2025
Guarding the Privacy of Label-Only Access to Neural Network Classifiers via iDP Verification Anan Kabaha Dana Drachsler-Cohen AAML 53 0 0 23 Feb 2025
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning Saber Malekmohammadi Yaoliang Yu Yang Cao FedML 88 6 0 17 Feb 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 48 0 0 03 Feb 2025
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 41 0 0 03 Oct 2024
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning Ning Wang Shanghao Shi Yang Xiao Yimin Chen Y. T. Hou W. Lou FedML AAML 48 1 0 12 Jul 2024
How Does Quantization Affect Multilingual LLMs? Kelly Marchisio Saurabh Dash Hongyu Chen Dennis Aumiller Ahmet Üstün Sara Hooker Sebastian Ruder MQ 52 9 0 03 Jul 2024
P4: Towards private, personalized, and Peer-to-Peer learning Mohammad Maheri S. Siby Sina Abdollahi Anastasia Borovykh Hamed Haddadi 26 0 0 27 May 2024
On Fairness of Low-Rank Adaptation of Large Models Zhoujie Ding Ken Ziyu Liu Pura Peetathawatchai Berivan Isik Sanmi Koyejo 48 4 0 27 May 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
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance Wenqi Wei Ling Liu 31 16 0 02 Feb 2024
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach Mahrokh Ghoddousi Boroujeni Andreas Krause Giancarlo Ferrari-Trecate FedML 34 3 0 16 Jan 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 52 2 0 07 Dec 2023
All Rivers Run to the Sea: Private Learning with Asymmetric Flows Yue Niu Ramy E. Ali Saurav Prakash Salman Avestimehr FedML 38 2 0 05 Dec 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
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 57 11 0 06 Jun 2023
Training Private Models That Know What They Don't Know Stephan Rabanser Anvith Thudi Abhradeep Thakurta Krishnamurthy Dvijotham Nicolas Papernot 26 7 0 28 May 2023
On the Fairness Impacts of Private Ensembles Models Cuong Tran Ferdinando Fioretto 41 4 0 19 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
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training Dmitrii Usynin Daniel Rueckert Georgios Kaissis FedML 28 2 0 04 May 2023
(Local) Differential Privacy has NO Disparate Impact on Fairness Héber H. Arcolezi K. Makhlouf C. Palamidessi 42 6 0 25 Apr 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 41 1 0 20 Apr 2023
Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects Conrad Sanderson David M. Douglas Qinghua Lu 43 12 0 17 Apr 2023
Fairness-aware Differentially Private Collaborative Filtering Zhenhuan Yang Yingqiang Ge Congzhe Su Dingxian Wang Xiaoting Zhao Yiming Ying FedML 37 3 0 16 Mar 2023
Arbitrary Decisions are a Hidden Cost of Differentially Private Training B. Kulynych Hsiang Hsu Carmela Troncoso Flavio du Pin Calmon 28 18 0 28 Feb 2023
Membership Inference Attacks against Synthetic Data through Overfitting Detection B. V. Breugel Hao Sun Zhaozhi Qian M. Schaar 33 45 0 24 Feb 2023
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI Juexiao Zhou Longxi Zhou Di Wang Xiaopeng Xu Haoyang Li Yuetan Chu Wenkai Han Xin Gao 33 20 0 20 Feb 2023
Multi-Task Differential Privacy Under Distribution Skew Walid Krichene Prateek Jain Shuang Song Mukund Sundararajan Abhradeep Thakurta Li Zhang FedML 43 3 0 15 Feb 2023
An Empirical Analysis of Fairness Notions under Differential Privacy Anderson Santana de Oliveira Caelin Kaplan Khawla Mallat Tanmay Chakraborty FedML 21 7 0 06 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging Soroosh Tayebi Arasteh Alexander Ziller Christiane Kuhl Marcus R. Makowski S. Nebelung R. Braren Daniel Rueckert Daniel Truhn Georgios Kaissis MedIm 39 18 0 03 Feb 2023
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation Noel Loo Ramin Hasani Mathias Lechner Alexander Amini Daniela Rus DD 52 5 0 02 Feb 2023
Context-Aware Differential Privacy for Language Modeling M. H. Dinh Ferdinando Fioretto 33 2 0 28 Jan 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
Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracy Yongqiang Wang Tamer Basar 31 21 0 14 Dec 2022
Tensions Between the Proxies of Human Values in AI Teresa Datta D. Nissani Max Cembalest Akash Khanna Haley Massa John P. Dickerson 36 2 0 14 Dec 2022
Fairness Increases Adversarial Vulnerability Cuong Tran Keyu Zhu Ferdinando Fioretto Pascal Van Hentenryck 34 6 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
Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version) Lucas Lange Maja Schneider Peter Christen Erhard Rahm 24 7 0 21 Nov 2022
On the Alignment of Group Fairness with Attribute Privacy Jan Aalmoes Vasisht Duddu A. Boutet 43 2 0 18 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 32 0 0 14 Nov 2022
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity Faisal Hamman Jiahao Chen Sanghamitra Dutta 25 9 0 03 Nov 2022
Differential Privacy has Bounded Impact on Fairness in Classification Paul Mangold Michaël Perrot A. Bellet Marc Tommasi 36 17 0 28 Oct 2022
On the utility and protection of optimization with differential privacy and classic regularization techniques Eugenio Lomurno Matteo matteucci 43 9 0 07 Sep 2022
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application Ramit Sawhney A. Neerkaje Ivan Habernal Lucie Flek 29 3 0 05 Sep 2022
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction Julien Ferry Ulrich Aïvodji Sébastien Gambs Marie-José Huguet Mohamed Siala AAML 42 14 0 02 Sep 2022