A Theory of Pricing Private Data

TL;DR

This paper develops a theoretical framework for pricing private data based on the accuracy of noisy query answers, enabling fair compensation for data owners and integrating principles from differential privacy and data markets.

Contribution

It introduces a novel model for assigning prices to private data queries and dividing revenue among data owners, extending existing privacy and data market theories.

Findings

Characterizes properties of valid pricing functions.

Proposes a method for fair revenue division.

Extends differential privacy principles to data markets.

Abstract

Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual's data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties. As the awareness of the value of the personal data increases, so has the drive to compensate the end user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means. In this paper we propose a theoretical framework for assigning prices to noisy query answers, as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the price function and micro-payments, and characterize valid solutions.