Establishing the Price of Privacy in Federated Data Trading

TL;DR

This paper introduces a model for federated data trading that incorporates differential privacy, proposing a pricing mechanism and revenue distribution to balance privacy protection with data utility, encouraging cooperation among data providers.

Contribution

It presents a novel federated data trading model with differential privacy, a pricing technique for private data, and a revenue-sharing mechanism to promote cooperation.

Findings

The proposed model effectively balances privacy and utility.

The revenue-distribution mechanism encourages data provider cooperation.

Experiments show benefits for both data providers and consumers.

Abstract

Personal data is becoming one of the most essential resources in today's information-based society. Accordingly, there is a growing interest in data markets, which operate data trading services between data providers and data consumers. One issue the data markets have to address is that of the potential threats to privacy. Usually some kind of protection must be provided, which generally comes to the detriment of utility. A correct pricing mechanism for private data should therefore depend on the level of privacy. In this paper, we propose a model of data federation in which data providers, who are, generally, less influential on the market than data consumers, form a coalition for trading their data, simultaneously shielding against privacy threats by means of differential privacy. Additionally, we propose a technique to price private data, and an revenue-distribution mechanism to distribute the revenue fairly in such federation data trading environments. Our model also motivates the data providers to cooperate with their respective federations, facilitating a fair and swift private data trading process. We validate our result through various experiments, showing that the proposed methods provide benefits to both data providers and consumers.