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Revealing Network Structure, Confidentially: Improved Rates for
  Node-Private Graphon Estimation

Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation

4 October 2018
C. Borgs
J. Chayes
Adam D. Smith
Ilias Zadik
    FedML
ArXivPDFHTML

Papers citing "Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation"

15 / 15 papers shown
Title
Low degree conjecture implies sharp computational thresholds in stochastic block model
Low degree conjecture implies sharp computational thresholds in stochastic block model
Jingqiu Ding
Yiding Hua
Lucas Slot
David Steurer
77
1
0
20 Feb 2025
Fully Dynamic Graph Algorithms with Edge Differential Privacy
Fully Dynamic Graph Algorithms with Edge Differential Privacy
Sofya Raskhodnikova
Teresa Anna Steiner
43
1
0
26 Sep 2024
Time-Aware Projections: Truly Node-Private Graph Statistics under
  Continual Observation
Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation
Palak Jain
Adam D. Smith
Connor Wagaman
37
5
0
07 Mar 2024
Node-Differentially Private Estimation of the Number of Connected
  Components
Node-Differentially Private Estimation of the Number of Connected Components
Iden Kalemaj
Sofya Raskhodnikova
Adam D. Smith
Charalampos E. Tsourakakis
45
7
0
12 Apr 2023
Private estimation algorithms for stochastic block models and mixture
  models
Private estimation algorithms for stochastic block models and mixture models
Hongjie Chen
Vincent Cohen-Addad
Tommaso dÓrsi
Alessandro Epasto
Jacob Imola
David Steurer
Stefan Tiegel
FedML
43
20
0
11 Jan 2023
OpBoost: A Vertical Federated Tree Boosting Framework Based on
  Order-Preserving Desensitization
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Xiaochen Li
Yuke Hu
Weiran Liu
Hanwen Feng
Li Peng
Yuan Hong
Kui Ren
Zhan Qin
FedML
132
26
0
04 Oct 2022
Archimedes Meets Privacy: On Privately Estimating Quantiles in High
  Dimensions Under Minimal Assumptions
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions
Omri Ben-Eliezer
Dan Mikulincer
Ilias Zadik
FedML
63
7
0
15 Aug 2022
Privately Estimating Graph Parameters in Sublinear time
Privately Estimating Graph Parameters in Sublinear time
Jeremiah Blocki
Elena Grigorescu
Tamalika Mukherjee
17
10
0
11 Feb 2022
Asymptotics of $\ell_2$ Regularized Network Embeddings
Asymptotics of ℓ2\ell_2ℓ2​ Regularized Network Embeddings
A. Davison
33
0
0
05 Jan 2022
Robust Estimation for Random Graphs
Robust Estimation for Random Graphs
Jayadev Acharya
Ayush Jain
Gautam Kamath
A. Suresh
Huanyu Zhang
30
8
0
09 Nov 2021
Modularity maximisation for graphons
Modularity maximisation for graphons
F. Klimm
N. Jones
Michael T. Schaub
29
1
0
02 Jan 2021
Optimal Private Median Estimation under Minimal Distributional
  Assumptions
Optimal Private Median Estimation under Minimal Distributional Assumptions
Christos Tzamos
Emmanouil-Vasileios Vlatakis-Gkaragkounis
Ilias Zadik
33
21
0
12 Nov 2020
A Primer on Private Statistics
A Primer on Private Statistics
Gautam Kamath
Jonathan R. Ullman
38
48
0
30 Apr 2020
Private Identity Testing for High-Dimensional Distributions
Private Identity Testing for High-Dimensional Distributions
C. Canonne
Gautam Kamath
Audra McMillan
Jonathan R. Ullman
Lydia Zakynthinou
37
36
0
28 May 2019
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
Adam Sealfon
Jonathan R. Ullman
15
45
0
24 May 2019
1