A Framework for Private Matrix Analysis in Sliding Window Model
Authors: Jalaj Upadhyay, Sarvagya Upadhyay
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We perform a rigorous study of private matrix analysis when only the last W updates to matrices are considered useful for analysis. We show the existing framework in the non-private setting is not robust to noise required for privacy. We then propose a framework robust to noise and use it to give first efficient o(W) space differentially private algorithms for spectral approximation, principal component analysis (PCA), multi-response linear regression, sparse PCA, and non-negative PCA. Prior to our work, no such result was known for sparse and non-negative differentially private PCA even in the static data setting. We also give a lower bound to demonstrate the cost of privacy. |
| Researcher Affiliation | Industry | 1Apple, USA (work done when the author was between jobs). 2Fujitsu Research of America, USA. Correspondence to: Jalaj Upadhyay <jalaj@apple.com>. |
| Pseudocode | Yes | The maintenance phase (high-level description of this phase is provided in Algorithm 1) ensures that the final set of matrices satisfies -approximate spectral histogram property. |
| Open Source Code | No | The paper does not provide a specific repository link, explicit code release statement, or mention of code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets, hence no information about public datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with datasets, therefore it does not provide information about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware. No hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and theoretical properties, not implementation details or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe practical experimental setups, hyperparameter values, or training configurations. |