# Distributed Differential Privacy By Sampling

## TL;DR

This paper introduces a distributed differential privacy mechanism based solely on sampling, maintaining utility and reducing privacy leakage in semi-honest settings compared to traditional randomized response methods.

## Contribution

It presents a novel sampling-based approach for distributed differential privacy that preserves utility and offers improved privacy guarantees over existing mechanisms.

## Key findings

- Utility remains constant and unaffected by variance.
- Smaller privacy leakage compared to randomized response.
- Effective in semi-honest settings.

## Abstract

In this paper, we describe our approach to achieve distributed differential privacy by sampling alone. Our mechanism works in the semi-honest setting (honest-but-curious whereby aggregators attempt to peek at the data though follow the protocol). We show that the utility remains constant and does not degrade due to the variance as compared to the randomized response mechanism. In addition, we show smaller privacy leakage as compared to the randomized response mechanism.