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DPlis: Boosting Utility of Differentially Private Deep Learning via
Randomized Smoothing

DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing

2 March 2021

Papers citing "DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing"

10 / 10 papers shown

Title
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang Zhiqi Bu Borja Balle Mingyi Hong Meisam Razaviyayn Vahab Mirrokni 88 2 0 04 Oct 2024
Differentially Private Language Models Benefit from Public Pre-training Gavin Kerrigan Dylan Slack Jens Tuyls 39 56 0 13 Sep 2020
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 165 179 0 28 Jul 2020
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM Bao Wang Quanquan Gu M. Boedihardjo Farzin Barekat Stanley J. Osher 59 25 0 28 Jun 2019
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu Wang Borja Balle S. Kasiviswanathan 46 397 0 31 Jul 2018
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning W. Wen Yandan Wang Feng Yan Cong Xu Chunpeng Wu Yiran Chen H. Li 37 51 0 21 May 2018
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 63 769 0 06 Nov 2016
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 191 4,075 0 18 Oct 2016
Conversational Contextual Cues: The Case of Personalization and History for Response Ranking Rami Al-Rfou Marc Pickett J. Snaider Yun-hsuan Sung B. Strope R. Kurzweil HAI 53 92 0 01 Jun 2016
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Saeed Ghadimi Guanghui Lan ODL 43 1,538 0 22 Sep 2013

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