# Anonymized Local Privacy

## TL;DR

This paper introduces Anonymized Local Privacy mechanisms that use linear noise and a three-value output space to protect individual privacy in distributed networks while maintaining data accuracy.

## Contribution

It proposes a novel privacy mechanism with a simple output space and analyzes its effectiveness in distributed settings with real data.

## Key findings

- Effective privacy protection through linear noise addition
- Suitable for distributed on-demand networks
- Maintains accuracy while safeguarding privacy

## Abstract

In this paper, we introduce the family of Anonymized Local Privacy mechanisms. These mechanisms have an output space of three values "Yes", "No", or "$\perp$" (not participating) and leverage the law of large numbers to generate linear noise in the number of data owners to protect privacy both before and after aggregation yet preserve accuracy. We describe the suitability in a distributed on-demand network and evaluate over a real dataset as we scale the population.