Participation Cost Estimation: Private Versus Non-Private Study

TL;DR

This paper compares private and non-private methods for estimating crowd levels using user location data, demonstrating that privacy-preserving mechanisms can incentivize user participation effectively.

Contribution

It introduces an analysis of privacy-preserving mechanisms in real-time crowd estimation, highlighting their benefits in encouraging user participation.

Findings

Privacy mechanisms incentivize user sharing.

Private data collection can match non-private accuracy.

User participation increases with privacy protections.

Abstract

In our study, we seek to learn the real-time crowd levels at popular points of interests based on users continually sharing their location data. We evaluate the benefits of users sharing their location data privately and non-privately, and show that suitable privacy-preserving mechanisms provide incentives for user participation in a private study as compared to a non-private study.