Differentially Private Empirical Risk Minimization: Efficient Algorithms
and Tight Error Bounds

Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

27 May 2014

Abhradeep Thakurta

Papers citing "Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds"

15 / 15 papers shown

Title
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees A. Banerjee Qiaobo Li Yingxue Zhou 92 0 0 11 Jun 2024
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar MLT 41 192 0 12 Jun 2020
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot Martín Abadi Ulfar Erlingsson Ian Goodfellow Kunal Talwar 58 1,012 0 18 Oct 2016
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy A. Beimel Kobbi Nissim Uri Stemmer 57 194 0 10 Jul 2014
Characterizing the Sample Complexity of Private Learners A. Beimel Kobbi Nissim Uri Stemmer 48 78 0 10 Feb 2014
Fingerprinting Codes and the Price of Approximate Differential Privacy Mark Bun Jonathan R. Ullman Salil P. Vadhan FedML 45 211 0 13 Nov 2013
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 134 573 0 08 Dec 2012
The Power of Linear Reconstruction Attacks S. Kasiviswanathan M. Rudelson Adam D. Smith AAML 80 54 0 08 Oct 2012
Near-Optimal Algorithms for Differentially-Private Principal Components Kamalika Chaudhuri Anand D. Sarwate Kaushik Sinha 56 153 0 12 Jul 2012
Lower bounds in differential privacy Anindya De 51 132 0 12 Jul 2011
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization Alekh Agarwal Peter L. Bartlett Pradeep Ravikumar Martin J. Wainwright 123 248 0 03 Sep 2010
Differentially Private Empirical Risk Minimization Kamalika Chaudhuri C. Monteleoni Anand D. Sarwate 93 1,482 0 01 Dec 2009
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning Benjamin I. P. Rubinstein Peter L. Bartlett Ling Huang N. Taft 83 293 0 30 Nov 2009
On the Geometry of Differential Privacy Moritz Hardt Kunal Talwar 94 462 0 21 Jul 2009
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 99 1,459 0 06 Mar 2008