# Trading Location Data with Bounded Personalized Privacy Loss

## TL;DR

This paper introduces a framework for trading personal location data with personalized privacy guarantees, addressing technical challenges like budget allocation and arbitrage-freeness, and proposing two mechanisms with distinct benefits.

## Contribution

It presents the first arbitrage-free trading mechanisms for location data that incorporate personalized privacy bounds, balancing data utility and privacy.

## Key findings

- Two arbitrage-free trading mechanisms proposed
- Mechanisms accommodate personalized privacy loss bounds
- Framework ensures limited privacy loss per individual

## Abstract

As personal data have been the new oil of the digital era, there is a growing trend perceiving personal data as a commodity. Although some people are willing to trade their personal data for money, they might still expect limited privacy loss, and the maximum tolerable privacy loss varies with each individual. In this paper, we propose a framework that enables individuals to trade their personal data with bounded personalized privacy loss, which raises technical challenges in the aspects of budget allocation and arbitrage-freeness. To deal with those challenges,we propose two arbitrage-free trading mechanisms with different advantages.