How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market

TL;DR

This paper introduces a practical personal data trading framework that balances privacy concerns and monetary compensation, incorporating user preferences, key principles, and a balanced pricing mechanism evaluated through experiments.

Contribution

It proposes a novel personal data trading framework with a balanced pricing mechanism that considers privacy loss and monetary compensation, addressing limitations of previous differential privacy-based models.

Findings

The proposed pricing mechanism effectively balances privacy and monetary compensation.

User survey insights inform the design of the trading framework.

Experimental results show improved fairness and efficiency over previous mechanisms.

Abstract

A personal data market is a platform including three participants: data owners (individuals), data buyers and market maker. Data owners who provide personal data are compensated according to their privacy loss. Data buyers can submit a query and pay for the result according to their desired accuracy. Market maker coordinates between data owner and buyer. This framework has been previously studied based on differential privacy. However, the previous study assumes data owners can accept any level of privacy loss and data buyers can conduct the transaction without regard to the financial budget. In this paper, we propose a practical personal data trading framework that is able to strike a balance between money and privacy. In order to gain insights on user preferences, we first conducted an online survey on human attitude to- ward privacy and interest in personal data trading. Second, we identify the 5 key principles of personal data market, which is important for designing a reasonable trading frame- work and pricing mechanism. Third, we propose a reason- able trading framework for personal data which provides an overview of how the data is traded. Fourth, we propose a balanced pricing mechanism which computes the query price for data buyers and compensation for data owners (whose data are utilized) as a function of their privacy loss. The main goal is to ensure a fair trading for both parties. Finally, we will conduct an experiment to evaluate the output of our proposed pricing mechanism in comparison with other previously proposed mechanism.