Subsampled Rényi Differential Privacy and Analytical Moments Accountant

31 July 2018

Papers citing "Subsampled Rényi Differential Privacy and Analytical Moments Accountant"

27 / 27 papers shown

Title
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data Kanishka Ranaweera Dinh C. Nguyen P. Pathirana David B. Smith Ming Ding Thierry Rakotoarivelo A. Seneviratne 70 0 0 27 Mar 2025
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models Kyeongkook Seo Dong-Jun Han Jaejun Yoo 84 0 0 11 Mar 2025
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions Gokularam Muthukrishnan Sheetal Kalyani 107 0 0 28 Jan 2025
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang Zhiqi Bu Borja Balle Mingyi Hong Meisam Razaviyayn Vahab Mirrokni 99 2 0 04 Oct 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 64 0 0 31 Jul 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 79 10 0 27 May 2024
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation Jie Xu Karthikeyan P. Saravanan Rogier van Dalen Haaris Mehmood David Tuckey Mete Ozay 103 6 0 10 May 2024
PRIMO: Private Regression in Multiple Outcomes Seth Neel 47 0 0 07 Mar 2023
Understanding Unintended Memorization in Federated Learning Om Thakkar Swaroop Indra Ramaswamy Rajiv Mathews Franccoise Beaufays FedML 45 47 0 12 Jun 2020
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 64 384 0 04 Jul 2018
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising Borja Balle Yu Wang MLT 47 395 0 16 May 2018
Rényi Differential Privacy Mechanisms for Posterior Sampling J. Geumlek Shuang Song Kamalika Chaudhuri 40 57 0 02 Oct 2017
Per-instance Differential Privacy Yu Wang 85 5 0 24 Jul 2017
Renyi Differential Privacy Ilya Mironov 53 1,243 0 24 Feb 2017
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 170 6,069 0 01 Jul 2016
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds Mark Bun Thomas Steinke 60 823 0 06 May 2016
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis James R. Foulds J. Geumlek Max Welling Kamalika Chaudhuri 47 102 0 23 Mar 2016
Concentrated Differential Privacy Cynthia Dwork G. Rothblum 52 446 0 06 Mar 2016
Differentially Private Release and Learning of Threshold Functions Mark Bun Kobbi Nissim Uri Stemmer Salil P. Vadhan 68 195 0 28 Apr 2015
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo Yu Wang S. Fienberg Alex Smola 46 248 0 26 Feb 2015
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle Yu Wang Jing Lei S. Fienberg 49 103 0 23 Feb 2015
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response Ulfar Erlingsson Vasyl Pihur Aleksandra Korolova 62 1,977 0 25 Jul 2014
Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds Raef Bassily Adam D. Smith Abhradeep Thakurta FedML 103 371 0 27 May 2014
Characterizing the Sample Complexity of Private Learners A. Beimel Kobbi Nissim Uri Stemmer 58 78 0 10 Feb 2014
The Composition Theorem for Differential Privacy Peter Kairouz Sewoong Oh Pramod Viswanath 91 677 0 04 Nov 2013
Rényi Divergence and Kullback-Leibler Divergence T. Erven P. Harremoes 63 1,326 0 12 Jun 2012
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 99 1,459 0 06 Mar 2008