A Market for Unbiased Private Data: Paying Individuals According to their Privacy Attitudes

TL;DR

This paper proposes a market mechanism that fairly compensates individuals for their private data based on their privacy attitudes, ensuring unbiased sampling and cost efficiency for data buyers.

Contribution

It introduces a realistic privacy-aware data market model and a mechanism that adjusts payments according to individual risk attitudes, promoting unbiased data collection.

Findings

The mechanism ensures unbiased sampling aligned with privacy attitudes.

Participants are compensated according to their privacy risk preferences.

Buyers pay less than uniform compensation models while maintaining data quality.

Abstract

Since there is, in principle, no reason why third parties should not pay individuals for the use of their data, we introduce a realistic market that would allow these payments to be made while taking into account the privacy attitude of the participants. And since it is usually important to use unbiased samples to obtain credible statistical results, we examine the properties that such a market should have and suggest a mechanism that compensates those individuals that participate according to their risk attitudes. Equally important, we show that this mechanism also benefits buyers, as they pay less for the data than they would if they compensated all individuals with the same maximum fee that the most concerned ones expect.