Privately Learning Subspaces
Authors: Vikrant Singhal, Thomas Steinke
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present differentially private algorithms that take input data sampled from a low-dimensional linear subspace (possibly with a small amount of error) and output that subspace (or an approximation to it). Theorem 1.1 (Main Result Exact Case). For all n, d, k, ℓ N and ε, δ > 0 satisfying n O ℓ+ log(1/δ) ε , there exists a randomized algorithm M : Rd n Sk d satisfying the following. Algorithm 1: DP Exact Subspace Estimator Algorithm 2: DP Approximate Subspace Estimator |
| Researcher Affiliation | Collaboration | Vikrant Singhal Cheriton School of Computer Science University of Waterloo Waterloo, ON N2L 3G1, Canada vikrant.singhal@uwaterloo.ca Thomas Steinke Google Research, Brain Team Mountain View, CA, United States of America subspace@thomas-steinke.net |
| Pseudocode | Yes | Algorithm 1: DP Exact Subspace Estimator DPESEε,δ,k,ℓ(X) Algorithm 2: DP Approximate Subspace Estimator DPASEε,δ,α,γ,k(X) |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes theoretical algorithms and their mathematical properties, assuming data is sampled from a distribution (e.g., 'data comes from a Gaussian distribution'). It does not use or mention any specific public or open dataset. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe any experiments that would involve training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies or specific version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |