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Individual Privacy Accounting via a Renyi Filter

Neural Information Processing Systems (NeurIPS), 2025

25 August 2020

Vitaly Feldman

Tijana Zrnic

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Papers citing "Individual Privacy Accounting via a Renyi Filter"

50 / 57 papers shown

Title
Accuracy-First Rényi Differential Privacy and Post-Processing Immunity Ossi Raisa A. Koskela Antti Honkela 0 0 0 26 Sep 2025
Private Hyperparameter Tuning with Ex-Post Guarantee Badih Ghazi Pritish Kamath Alexander Knop Ravi Kumar Pasin Manurangsi Chiyuan Zhang 36 1 0 21 Aug 2025
Big Bird: Privacy Budget Management for W3C's Privacy-Preserving Attribution API Pierre Tholoniat Alison Caulfield Giorgio Cavicchioli Mark Chen Nikos Goutzoulias Benjamin Case Asaf Cidon Roxana Geambasu Mathias Lécuyer Martin Thomson 157 0 0 05 Jun 2025
Leveraging Per-Instance Privacy for Machine Unlearning N. Sepahvand Anvith Thudi Berivan Isik Ashmita Bhattacharyya Nicolas Papernot Eleni Triantafillou Daniel M. Roy Gintare Karolina Dziugaite MU FedML 135 0 0 24 May 2025
Controlled privacy leakage propagation throughout overlapping grouped learning Shahrzad Kiani Franziska Boenisch S. Draper FedML 166 0 0 06 Mar 2025
On the Differential Privacy and Interactivity of Privacy Sandbox Reports Badih Ghazi Charlie Harrison Arpana Hosabettu Pritish Kamath Alexander Knop ... Ethan Leeman Pasin Manurangsi Vikas Sahu Vikas Sahu Phillipp Schoppmann 259 3 0 22 Dec 2024
Adaptively Private Next-Token Prediction of Large Language Models James Flemings Meisam Razaviyayn Murali Annavaram 192 2 0 02 Oct 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 192 0 0 31 Jul 2024
Adaptive Data Analysis for Growing Data Neil G. Marchant Benjamin I. P. Rubinstein 80 0 0 22 May 2024
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning Jonathan Lebensold Maziar Sanjabi Pietro Astolfi Adriana Romero Soriano Kamalika Chaudhuri Mike Rabbat Chuan Guo DiffM 130 6 0 21 Mar 2024
Privacy Amplification for the Gaussian Mechanism via Bounded Support Shengyuan Hu Saeed Mahloujifar Virginia Smith Kamalika Chaudhuri Chuan Guo FedML 121 1 0 07 Mar 2024
On the Privacy of Selection Mechanisms with Gaussian Noise Jonathan Lebensold Doina Precup Borja Balle 168 1 0 09 Feb 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu Jian Lou Li Xiong Jinfei Liu Xiaofeng Meng 280 13 0 29 Jan 2024
SoK: Memorisation in machine learning Dmitrii Usynin Moritz Knolle Georgios Kaissis 161 1 0 06 Nov 2023
Privately Answering Queries on Skewed Data via Per Record Differential Privacy Jeremy Seeman William Sexton David Pujol Ashwin Machanavajjhala 109 5 0 19 Oct 2023
Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters Samuel Haney Michael Shoemate Grace Tian Salil P. Vadhan Andrew Vyrros Vicki Xu Wanrong Zhang 157 8 0 12 Sep 2023
Personalized Privacy Amplification via Importance Sampling Dominik Fay Sebastian Mair Jens Sjölund 226 0 0 05 Jul 2023
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD Anvith Thudi Hengrui Jia Casey Meehan Ilia Shumailov Nicolas Papernot 221 10 0 01 Jul 2023
Adaptive Privacy Composition for Accuracy-first Mechanisms Ryan M. Rogers G. Samorodnitsky Zhiwei Steven Wu Aaditya Ramdas 120 4 0 24 Jun 2023
"Private Prediction Strikes Back!'' Private Kernelized Nearest Neighbors with Individual Renyi Filter Yuqing Zhu Xuandong Zhao Chuan Guo Yu-Xiang Wang 133 4 0 12 Jun 2023
Personalized DP-SGD using Sampling Mechanisms Geon Heo Junseok Seo Steven Euijong Whang 101 2 0 24 May 2023
Have it your way: Individualized Privacy Assignment for DP-SGD Franziska Boenisch Christopher Muhl Adam Dziedzic Roy Rinberg Nicolas Papernot 160 20 0 29 Mar 2023
Subsampling Suffices for Adaptive Data Analysis Guy Blanc 108 12 0 17 Feb 2023
Multi-Task Differential Privacy Under Distribution Skew Walid Krichene Prateek Jain Shuang Song Mukund Sundararajan Abhradeep Thakurta Li Zhang FedML 120 3 0 15 Feb 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 177 52 0 14 Feb 2023
Cohere: Managing Differential Privacy in Large Scale Systems Nicolas Küchler Emanuel Opel Hidde Lycklama Alexander Viand Anwar Hithnawi 125 5 0 20 Jan 2023
Lower Bounds for Rényi Differential Privacy in a Black-Box Setting T. Kutta Önder Askin Martin Dunsche 116 6 0 09 Dec 2022
How Do Input Attributes Impact the Privacy Loss in Differential Privacy? Tamara T. Mueller Stefan Kolek F. Jungmann Alexander Ziller Dmitrii Usynin Moritz Knolle Daniel Rueckert Georgios Kaissis 143 3 0 18 Nov 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 241 63 0 02 Oct 2022
Individual Privacy Accounting with Gaussian Differential Privacy A. Koskela Marlon Tobaben Antti Honkela 176 23 0 30 Sep 2022
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census Daniel Kifer John M. Abowd Robert Ashmead Ryan Cumings-Menon Philip Leclerc Ashwin Machanavajjhala William Sexton Pavel I Zhuravlev 161 29 0 07 Sep 2022
Composition Theorems for Interactive Differential Privacy Xin Lyu 120 21 0 19 Jul 2022
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions Vadym Doroshenko Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 131 47 0 10 Jul 2022
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints Justin Whitehouse Zhiwei Steven Wu Aaditya Ramdas Ryan M. Rogers 193 12 0 15 Jun 2022
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging Edwige Cyffers Mathieu Even A. Bellet Laurent Massoulié FedML 221 27 0 10 Jun 2022
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu Gautam Kamath Janardhan Kulkarni Tie-Yan Liu Jian Yin Huishuai Zhang 241 22 0 06 Jun 2022
$Privacy accounting $\varepsilon$conomics: Improving differential privacy composition via a posteriori bounds$ Privacy accounting $\varepsilon$ conomics: Improving differential privacy composition via a posteriori bounds Valentin Hartmann Vincent Bindschaedler Alexander Bentkamp Robert West 123 1 0 06 May 2022
What You See is What You Get: Principled Deep Learning via Distributional Generalization B. Kulynych Yao-Yuan Yang Yaodong Yu Jarosław Błasiok Preetum Nakkiran OOD 155 11 0 07 Apr 2022
SoK: Differential Privacy on Graph-Structured Data Tamara T. Mueller Dmitrii Usynin Johannes C. Paetzold Daniel Rueckert Georgios Kaissis 134 18 0 17 Mar 2022
Fully Adaptive Composition in Differential Privacy Justin Whitehouse Aaditya Ramdas Ryan M. Rogers Zhiwei Steven Wu 180 42 0 10 Mar 2022
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees Franziska Boenisch Christopher Muhl Roy Rinberg Jannis Ihrig Adam Dziedzic 138 20 0 21 Feb 2022
Backpropagation Clipping for Deep Learning with Differential Privacy Timothy Stevens Ivoline C. Ngong David Darais Calvin Hirsch David Slater Joseph P. Near 86 10 0 10 Feb 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 188 184 0 13 Jan 2022
Submix: Practical Private Prediction for Large-Scale Language Models Antonio A. Ginart Laurens van der Maaten James Zou Chuan Guo 124 28 0 04 Jan 2022
Privately Publishable Per-instance Privacy Rachel Redberg Yu Wang 148 18 0 03 Nov 2021
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates Friedrich Dörmann Osvald Frisk L. Andersen Christian Fischer Pedersen FedML 177 28 0 12 Oct 2021
Towards General-purpose Infrastructure for Protecting Scientific Data Under Study Andrew Trask Kritika Prakash 160 3 0 04 Oct 2021
Partial sensitivity analysis in differential privacy Tamara T. Mueller Alexander Ziller Dmitrii Usynin Moritz Knolle F. Jungmann Daniel Rueckert Georgios Kaissis 162 1 0 22 Sep 2021
An automatic differentiation system for the age of differential privacy Dmitrii Usynin Alexander Ziller Moritz Knolle Andrew Trask Kritika Prakash Daniel Rueckert Georgios Kaissis 157 3 0 22 Sep 2021
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation Alexander Ziller Dmitrii Usynin Moritz Knolle Kritika Prakash Andrew Trask R. Braren Marcus R. Makowski Daniel Rueckert Georgios Kaissis 104 5 0 09 Jul 2021