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Privately Solving Linear Programs
International Colloquium on Automata, Languages and Programming (ICALP), 2014
15 February 2014
Justin Hsu
Aaron Roth
Tim Roughgarden
Jonathan R. Ullman
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Papers citing
"Privately Solving Linear Programs"
22 / 22 papers shown
Multi-Agent Distributed Optimization With Feasible Set Privacy
S. Meel
S. Ulukus
187
0
0
06 Oct 2025
Differential Privacy for Euclidean Jordan Algebra with Applications to Private Symmetric Cone Programming
Zhao Song
Jianfei Xue
Lichen Zhang
211
0
0
21 Sep 2025
Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers
International Conference on Learning Representations (ICLR), 2024
Oren Mangoubi
Nisheeth K. Vishnoi
140
0
0
06 Sep 2024
Fine-Grained Privacy Guarantees for Coverage Problems
Laxman Dhulipala
George Z. Li
176
1
0
05 Mar 2024
Differential Privacy via Distributionally Robust Optimization
Aras Selvi
Huikang Liu
W. Wiesemann
395
6
0
25 Apr 2023
Multi-Task Differential Privacy Under Distribution Skew
International Conference on Machine Learning (ICML), 2023
Walid Krichene
Prateek Jain
Shuang Song
Mukund Sundararajan
Abhradeep Thakurta
Li Zhang
FedML
277
3
0
15 Feb 2023
Differentially Private Partial Set Cover with Applications to Facility Location
International Joint Conference on Artificial Intelligence (IJCAI), 2022
George Z. Li
Dung Nguyen
A. Vullikanti
284
9
0
21 Jul 2022
Differential Privacy for Symbolic Systems with Application to Markov Chains
Bo Chen
Kevin J. Leahy
Austin M. Jones
Matthew T. Hale
417
18
0
07 Feb 2022
Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism
Samuel B. Hopkins
Gautam Kamath
Mahbod Majid
FedML
341
68
0
25 Nov 2021
Differentially Private Linear Optimization for Multi-Party Resource Sharing
Utku Karaca
N. Aydin
S. Yıldırım
S. Birbil
286
0
0
20 Oct 2021
Private Optimization Without Constraint Violations
Andrés Munoz Medina
Umar Syed
Sergei Vassilvitskii
Ellen Vitercik
251
15
0
02 Jul 2020
Differentially Private Convex Optimization with Feasibility Guarantees
V. Dvorkin
Ferdinando Fioretto
Pascal Van Hentenryck
J. Kazempour
Pierre Pinson
181
6
0
22 Jun 2020
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Neural Information Processing Systems (NeurIPS), 2020
Haim Kaplan
Yishay Mansour
Uri Stemmer
Eliad Tsfadia
235
17
0
16 Apr 2020
Differentially Private Optimal Power Flow for Distribution Grids
IEEE Transactions on Power Systems (IEEE Trans. Power Syst.), 2020
V. Dvorkin
Ferdinando Fioretto
Pascal Van Hentenryck
Pierre Pinson
J. Kazempour
216
63
0
08 Apr 2020
Efficient, Noise-Tolerant, and Private Learning via Boosting
Annual Conference Computational Learning Theory (COLT), 2020
Mark Bun
M. Carmosino
Jessica Sorrell
FedML
375
21
0
04 Feb 2020
Differential Privacy of Aggregated DC Optimal Power Flow Data
Fengyu Zhou
James Anderson
S. Low
134
26
0
27 Mar 2019
Private Center Points and Learning of Halfspaces
Annual Conference Computational Learning Theory (COLT), 2019
A. Beimel
Shay Moran
Kobbi Nissim
Uri Stemmer
268
33
0
27 Feb 2019
Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces
Neural Information Processing Systems (NeurIPS), 2019
Baoxiang Wang
N. Hegde
333
71
0
30 Jan 2019
Customized Local Differential Privacy for Multi-Agent Distributed Optimization
Roel Dobbe
Ye Pu
Jingge Zhu
Kannan Ramchandran
Claire Tomlin
203
15
0
15 Jun 2018
Calibration for the (Computationally-Identifiable) Masses
Úrsula Hébert-Johnson
Michael P. Kim
Omer Reingold
G. Rothblum
FaML
232
92
0
22 Nov 2017
Assessing the Privacy Cost in Centralized Event-Based Demand Response for Microgrids
A. Karapetyan
Syafiq Kamarul Azman
Z. Aung
331
10
0
04 Mar 2017
Differentially Private Convex Optimization with Piecewise Affine Objectives
IEEE Conference on Decision and Control (CDC), 2014
Shuo Han
Ufuk Topcu
George J. Pappas
235
46
0
24 Mar 2014
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