Majority Vote for Distributed Differentially Private Sign Selection

Autor:	Liu, Weidong, Tu, Jiyuan, Mao, Xiaojun, Chen, Xi
Rok vydání:	2022
Předmět:	Computer Science - Cryptography and Security Computer Science - Machine Learning Statistics - Methodology Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Privacy-preserving data analysis has become more prevalent in recent years. In this study, we propose a distributed group differentially private Majority Vote mechanism, for the sign selection problem in a distributed setup. To achieve this, we apply the iterative peeling to the stability function and use the exponential mechanism to recover the signs. For enhanced applicability, we study the private sign selection for mean estimation and linear regression problems, in distributed systems. Our method recovers the support and signs with the optimal signal-to-noise ratio as in the non-private scenario, which is better than contemporary works of private variable selections. Moreover, the sign selection consistency is justified by theoretical guarantees. Simulation studies are conducted to demonstrate the effectiveness of the proposed method. Comment: 41 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.04419 Zobrazit plný text záznamu View this record from Arxiv