How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

1 March 2023

Papers citing "How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy"

50 / 106 papers shown

Title
DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics Yifeng Mao Bozhidar Stevanoski Yves-Alexandre de Montjoye 52 0 0 25 Apr 2025
Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning Robert Chew Matthew R. Williams Elan A. Segarra Alexander J. Preiss Amanda Konet T. Savitsky 43 0 0 27 Mar 2025
Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation Junyi Zhu Ruicong Yao Taha Ceritli Savas Ozkan Matthew B. Blaschko Eunchung Noh Jeongwon Min Cho Jung Min Mete Ozay FedML 103 0 0 26 Mar 2025
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis Chen Gong Kecen Li Zinan Lin Tianhao Wang 61 3 0 18 Mar 2025
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 64 0 0 16 Mar 2025
Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs Bowen Tan Zheng Xu Eric P. Xing Zhiting Hu Shanshan Wu SyDa 87 0 0 16 Mar 2025
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees Juan Felipe Gomez B. Kulynych G. Kaissis Jamie Hayes Borja Balle Antti Honkela 56 0 0 13 Mar 2025
Probabilistic Reasoning with LLMs for k-anonymity Estimation Jonathan Zheng Sauvik Das Alan Ritter Wei-ping Xu 60 0 0 12 Mar 2025
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 55 0 0 21 Feb 2025
Private Text Generation by Seeding Large Language Model Prompts Supriya Nagesh Justin Y. Chen Nina Mishra Tal Wagner SyDa SILM 66 1 0 20 Feb 2025
Smoothed Normalization for Efficient Distributed Private Optimization Egor Shulgin Sarit Khirirat Peter Richtárik FedML 87 0 0 20 Feb 2025
The Curious Case of Arbitrariness in Machine Learning Prakhar Ganesh Afaf Taik G. Farnadi 64 2 0 28 Jan 2025
Advancing privacy in learning analytics using differential privacy Qinyi Liu Ronas Shakya Mohammad Khalil Jelena Jovanovic 44 1 0 03 Jan 2025
A Tale of Two Imperatives: Privacy and Explainability Supriya Manna Niladri Sett 159 0 0 30 Dec 2024
Balls-and-Bins Sampling for DP-SGD Lynn Chua Badih Ghazi Charlie Harrison Ethan Leeman Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 85 4 0 21 Dec 2024
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators Tejumade Afonja Hui-Po Wang Raouf Kerkouche Mario Fritz SyDa 118 2 0 03 Dec 2024
Preserving Expert-Level Privacy in Offline Reinforcement Learning Navodita Sharma Vishnu Vinod Abhradeep Thakurta Alekh Agarwal Borja Balle Christoph Dann A. Raghuveer OffRL 84 0 0 18 Nov 2024
Scalable DP-SGD: Shuffling vs. Poisson Subsampling Lynn Chua Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 41 5 0 06 Nov 2024
R+R:Understanding Hyperparameter Effects in DP-SGD Felix Morsbach J. Reubold T. Strufe 36 0 0 04 Nov 2024
Noise-Aware Differentially Private Variational Inference Talal Alrawajfeh Joonas Jälkö Antti Honkela 35 0 0 25 Oct 2024
Masked Differential Privacy David Schneider Sina Sajadmanesh Vikash Sehwag Saquib Sarfraz Rainer Stiefelhagen Lingjuan Lyu Vivek Sharma 33 1 0 22 Oct 2024
CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications Chaoran Chen Daodao Zhou Yanfang Ye Toby Jia-jun Li Yaxing Yao AILaw 41 3 0 17 Oct 2024
Federated Learning in Practice: Reflections and Projections Katharine Daly Hubert Eichner Peter Kairouz H. B. McMahan Daniel Ramage Zheng Xu FedML 53 5 0 11 Oct 2024
Privately Learning from Graphs with Applications in Fine-tuning Large Language Models Haoteng Yin Rongzhe Wei Eli Chien P. Li 33 0 0 10 Oct 2024
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD Thomas Steinke Milad Nasr Arun Ganesh Borja Balle Christopher A. Choquette-Choo Matthew Jagielski Jamie Hayes Abhradeep Thakurta Adam Smith Andreas Terzis 34 7 0 08 Oct 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 38 6 0 08 Oct 2024
Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models Hongbin Liu Lun Wang Om Thakkar Abhradeep Thakurta Arun Narayanan 37 0 0 02 Oct 2024
Training Large ASR Encoders with Differential Privacy Geeticka Chauhan Steve Chien Om Thakkar Abhradeep Thakurta Arun Narayanan 33 1 0 21 Sep 2024
Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm R. Teal Witter Christopher Musco 48 0 0 06 Sep 2024
Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation Lun Wang 34 0 0 29 Aug 2024
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning Raja Vavekanand Kira Sam 56 0 0 30 Jul 2024
Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation Doan Nam Long Vu Timour Igamberdiev Ivan Habernal 52 0 0 26 Jul 2024
Synthetic Trajectory Generation Through Convolutional Neural Networks Jesse Merhi Erik Buchholz S. Kanhere 37 0 0 24 Jul 2024
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning Luca Corbucci Mikko A. Heikkilä David Solans Noguero Anna Monreale Nicolas Kourtellis FedML 52 3 0 21 Jul 2024
Private prediction for large-scale synthetic text generation Kareem Amin Alex Bie Weiwei Kong Alexey Kurakin Natalia Ponomareva Umar Syed Andreas Terzis Sergei Vassilvitskii SyDa SILM 48 3 0 16 Jul 2024
Differentially Private Neural Network Training under Hidden State Assumption Ding Chen Chen Liu FedML 32 0 0 11 Jul 2024
Fine-Tuning Large Language Models with User-Level Differential Privacy Zachary Charles Arun Ganesh Ryan McKenna H. B. McMahan Nicole Mitchell Krishna Pillutla Keith Rush 39 11 0 10 Jul 2024
Attack-Aware Noise Calibration for Differential Privacy B. Kulynych Juan Felipe Gomez G. Kaissis Flavio du Pin Calmon Carmela Troncoso 57 6 0 02 Jul 2024
A Method to Facilitate Membership Inference Attacks in Deep Learning Models Zitao Chen Karthik Pattabiraman MIACV MLAU AAML MIALM 75 1 0 02 Jul 2024
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning Lynn Chua Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Daogao Liu Pasin Manurangsi Amer Sinha Chiyuan Zhang 34 11 0 20 Jun 2024
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs Charlie Hou Akshat Shrivastava Hongyuan Zhan Rylan Conway Trang Le Adithya Sagar Giulia Fanti Daniel Lazar 36 8 0 05 Jun 2024
The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity Prakhar Ganesh Ihsan Ibrahim Daldaban Ignacio Cofone G. Farnadi 54 2 0 28 May 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 53 9 0 27 May 2024
Banded Square Root Matrix Factorization for Differentially Private Model Training Nikita Kalinin Christoph H. Lampert 34 6 0 22 May 2024
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data Vikhyat Agrawal Sunil Vasu Kalmady Venkataseetharam Manoj Malipeddi Manisimha Manthena Weijie Sun Saiful Islam Abram Hindle Padma Kaul Russell Greiner FedML 27 5 0 26 Apr 2024
DNA: Differentially private Neural Augmentation for contact tracing Rob Romijnders Christos Louizos Yuki M. Asano Max Welling FedML 31 0 0 20 Apr 2024
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning Syed Mhamudul Hasan Abdur R. Shahid Ahmed Imteaj AAML 26 4 0 27 Mar 2024
How Private are DP-SGD Implementations? Lynn Chua Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 43 12 0 26 Mar 2024
Differentially Private Next-Token Prediction of Large Language Models James Flemings Meisam Razaviyayn Murali Annavaram 36 6 0 22 Mar 2024
Efficiently Computing Similarities to Private Datasets A. Backurs Zinan Lin S. Mahabadi Sandeep Silwal Jakub Tarnawski 73 4 0 13 Mar 2024