Logistics Regression Model for Differentially-Private Matrix Masked Data

Autor:	Nghiem, Linh H, Ding, Aidong A., Wu, Samuel
Rok vydání:	2024
Předmět:	Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	A recently proposed scheme utilizing local noise addition and matrix masking enables data collection while protecting individual privacy from all parties, including the central data manager. Statistical analysis of such privacy-preserved data is particularly challenging for nonlinear models like logistic regression. By leveraging a relationship between logistic regression and linear regression estimators, we propose the first valid statistical analysis method for logistic regression under this setting. Theoretical analysis of the proposed estimators confirmed its validity under an asymptotic framework with increasing noise magnitude to account for strict privacy requirements. Simulations and real data analyses demonstrate the superiority of the proposed estimators over naive logistic regression methods on privacy-preserved data sets.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.15520 Zobrazit plný text záznamu View this record from Arxiv