Optimized, Direct Sale of Privacy in Personal-Data Marketplaces

TL;DR

This paper analyzes a privacy-money trade-off mechanism in personal-data marketplaces, providing a theoretical framework and closed-form solutions for optimal data sharing strategies under a privacy measure.

Contribution

It introduces a formal model for privacy and monetary exchange, offering a parametric solution and characterizing the privacy-reward trade-off with Bregman divergences.

Findings

Derived a closed-form expression for optimal privacy-money trade-off.

Characterized the privacy-reward relationship for various divergence measures.

Provided insights into how users can optimize data sharing decisions.

Abstract

Very recently, we are witnessing the emergence of a number of start-ups that enables individuals to sell their private data directly to brokers and businesses. While this new paradigm may shift the balance of power between individuals and companies that harvest data, it raises some practical, fundamental questions for users of these services: how they should decide which data must be vended and which data protected, and what a good deal is. In this work, we investigate a mechanism that aims at helping users address these questions. The investigated mechanism relies on a hard-privacy model and allows users to share partial or complete profile data with broker companies in exchange for an economic reward. The theoretical analysis of the trade-off between privacy and money posed by such mechanism is the object of this work. We adopt a generic measure of privacy although part of our analysis focuses on some important examples of Bregman divergences. We find a parametric solution to the problem of optimal exchange of privacy for money, and obtain a closed-form expression and characterize the trade-off between profile-disclosure risk and economic reward for several interesting cases.