Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation
Authors: Di Wang, Jinhui Xu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the sparse covariance matrix estimation problem in the local differential privacy model, and give a non-trivial lower bound on the non-interactive private minimax risk in the metric of squared spectral norm. We show that the lower bound is actually tight, as it matches a previous upper bound. Our main technique for achieving this lower bound is a general framework, called General Private Assouad Lemma, which is a considerable generalization of the previous private Assouad lemma and can be used as a general method for bounding the private minimax risk of matrix-related estimation problems. |
| Researcher Affiliation | Academia | Di Wang , Jinhui Xu Department of Computer Science and Engineering State University of New York at Buffalo, NY, USA. {dwang45,jinhui}@buffalo.edu. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It focuses on theoretical proofs and mathematical derivations. |
| Open Source Code | No | The paper does not provide any statement about making its source code available or include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation on datasets. Therefore, it does not mention public datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation on datasets. Therefore, it does not mention validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental work. Therefore, it does not mention any hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental work. Therefore, it does not list any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments. Therefore, it does not provide details about an experimental setup, hyperparameters, or training configurations. |