Correlated Noise Provably Beats Independent Noise for Differentially
Private Learning

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Correlated Noise Provably Beats Independent Noise for Differentially Private Learning

10 October 2023

Christopher A. Choquette-Choo

Krishnamurthy Dvijotham

Krishna Pillutla

Abhradeep Thakurta

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Papers citing "Correlated Noise Provably Beats Independent Noise for Differentially Private Learning"

18 / 18 papers shown

Title
Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting Yifan Wu Jingze Shi Bingheng Wu Jiayi Zhang Xiaotian Lin Nan Tang Yuyu Luo LRM 92 1 0 26 May 2025
Differentially Private Online Federated Learning with Correlated Noise Jiaojiao Zhang Linglingzhi Zhu Mikael Johansson FedML 87 1 0 10 Jan 2025
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 123 9 0 08 Oct 2024
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang Zhiqi Bu Borja Balle Mingyi Hong Meisam Razaviyayn Vahab Mirrokni 123 2 0 04 Oct 2024
Federated Learning of Gboard Language Models with Differential Privacy Zheng Xu Yanxiang Zhang Galen Andrew Christopher A. Choquette-Choo Peter Kairouz H. B. McMahan Jesse Rosenstock Yuanbo Zhang FedML 88 81 0 29 May 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 63 9 0 30 Jan 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping Jiyan He Xuechen Li Da Yu Huishuai Zhang Janardhan Kulkarni Y. Lee A. Backurs Nenghai Yu Jiang Bian 104 48 0 03 Dec 2022
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams S. Denisov H. B. McMahan J. Rush Adam D. Smith Abhradeep Thakurta FedML 97 65 0 16 Feb 2022
Quantifying Memorization Across Neural Language Models Nicholas Carlini Daphne Ippolito Matthew Jagielski Katherine Lee Florian Tramèr Chiyuan Zhang PILM 124 630 0 15 Feb 2022
Anticorrelated Noise Injection for Improved Generalization Antonio Orvieto Hans Kersting F. Proske Francis R. Bach Aurelien Lucchi 109 48 0 06 Feb 2022
The Price of Differential Privacy under Continual Observation Palak Jain Sofya Raskhodnikova Satchit Sivakumar Adam D. Smith 75 52 0 01 Dec 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown Basel Alomair Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 509 1,946 0 14 Dec 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 183 1,452 0 29 Feb 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 611 4,905 0 23 Jan 2020
Hypothesis Testing Interpretations and Renyi Differential Privacy Borja Balle Gilles Barthe Marco Gaboardi Justin Hsu Tetsuya Sato 50 117 0 24 May 2019
On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables Julyan Arbel Olivier Marchal Hien Nguyen 80 28 0 26 Jan 2019
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 216 6,162 0 01 Jul 2016
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis R. Bach Eric Moulines 96 405 0 10 Jun 2013

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