Research map / Federated learning and privacy / Differential privacy
On Sparse Linear Regression in the Local Differential Privacy Model
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Keep labels-only privacy (Label-LDP-IHT) instead of whole-dataset LDP | Sparse linear regression under LDP · Synthetic data, varying dimensionality p | relative error ||θ_T-θ*||/||θ*|| | No clear effect |
| Increase privacy parameter epsilon (weaker privacy) | Label-LDP-IHT · Synthetic data, varying privacy level | relative error | Helped |
| Increase sparsity level s* | l0-constrained logistic regression DP-IHT · Covertype | optimality gap | Hurt |
| Decrease privacy parameter epsilon (stronger privacy) | DP-IHT logistic regression · Covertype | optimality gap | Hurt |
| Increase dimensionality p with fixed s and epsilon | DP-IHT logistic regression · Covertype | optimality gap | No clear effect |
| Label-privacy version (Label-LDP-IHT) vs whole-dataset privacy: error logarithmic vs polynomial in p | Sparse linear regression under LDP · theory | estimation error dependence on p | Helped |
Closest papers by meaning
- Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model (ICLR 2024 · similarity 0.96)
- Better Locally Private Sparse Estimation Given Multiple Samples Per User (ICML 2024 · similarity 0.93)
- On the Computational Complexity of Private High-dimensional Model Selection (NeurIPS 2024 · similarity 0.90)
- Locally Optimal Private Sampling: Beyond the Global Minimax (NeurIPS 2025 · similarity 0.90)
- Differentially Private Optimization with Sparse Gradients (NeurIPS 2024 · similarity 0.90)
- Truthful High Dimensional Sparse Linear Regression (NeurIPS 2024 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.