ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1803.02596
  4. Cited By
Revisiting differentially private linear regression: optimal and
  adaptive prediction & estimation in unbounded domain

Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain

7 March 2018
Yu Wang
ArXivPDFHTML

Papers citing "Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain"

3 / 3 papers shown
Title
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions
Gokularam Muthukrishnan
Sheetal Kalyani
107
0
0
28 Jan 2025
Distributed Differentially Private Data Analytics via Secure Sketching
Distributed Differentially Private Data Analytics via Secure Sketching
Jakob Burkhardt
Hannah Keller
Claudio Orlandi
Chris Schwiegelshohn
FedML
109
0
0
30 Nov 2024
PRIMO: Private Regression in Multiple Outcomes
PRIMO: Private Regression in Multiple Outcomes
Seth Neel
45
0
0
07 Mar 2023
1