# Majority Vote for Distributed Differentially Private Sign Selection

arXiv: https://arxiv.org/abs/2209.04419 · HTML: https://mlsanity.com/paper/2209.04419/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [Deep Learning with Gaussian Differential Privacy](https://mlsanity.com/paper/1911.11607/) (2019)
- [Minimax Optimal Procedures for Locally Private Estimation](https://mlsanity.com/paper/1604.02390/) (2016)
- [Confidence Intervals and Hypothesis Testing for High-Dimensional Regression](https://mlsanity.com/paper/1306.3171/) (2013)
- [Efficient Distributed Learning with Sparsity](https://mlsanity.com/paper/1605.07991/) (2016)
- [De-biasing the Lasso: Optimal Sample Size for Gaussian Designs](https://mlsanity.com/paper/1508.02757/) (2015)
- [Tight Lower Bounds for Differentially Private Selection](https://mlsanity.com/paper/1704.03024/) (2017)
- [Privacy-Preserving Dynamic Personalized Pricing with Demand Learning](https://mlsanity.com/paper/2009.12920/) (2020)
- [Learning Privately from Multiparty Data](https://mlsanity.com/paper/1602.03552/) (2016)
- [Oneshot Differentially Private Top-k Selection](https://mlsanity.com/paper/2105.08233/) (2021)
- [Differentially Private False Discovery Rate Control](https://mlsanity.com/paper/1807.04209/) (2018)
- [Differential Privacy in Personalized Pricing with Nonparametric Demand Models](https://mlsanity.com/paper/2109.04615/) (2021)
- [On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms](https://mlsanity.com/paper/1605.02277/) (2016)
- [The Price of Selection in Differential Privacy](https://mlsanity.com/paper/1702.02970/) (2017)
- [Least Squares Approximation for a Distributed System](https://mlsanity.com/paper/1908.04904/) (2019)
- [A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning](https://mlsanity.com/paper/1404.2644/) (2014)
- [Differentially Private Model Selection with Penalized and Constrained Likelihood](https://mlsanity.com/paper/1607.04204/) (2016)

## Built on by (most cited first, 1 of 1)

- [On the Computational Complexity of Private High-dimensional Model Selection](https://mlsanity.com/paper/2310.07852/) (2023)

## Abstract (arXiv)

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.