Adversarial Contract Design for Private Data Commercialization

TL;DR

This paper develops a contract-theoretic framework for private data commercialization, accounting for honest and adversarial buyers, and introduces the concept of Price of Adversary to quantify privacy risks and revenue impacts.

Contribution

It introduces an adversarial contract design framework and the novel Price of Adversary metric for privacy and revenue analysis in data markets.

Findings

Bounds on Price of Adversary for different adversary utilities

A fast approximation method for contract computation with adversaries

Quantitative analysis of adversarial impact on data seller revenue

Abstract

The proliferation of data collection and machine learning techniques has created an opportunity for commercialization of private data by data aggregators. In this paper, we study this data monetization problem using a contract-theoretic approach. Our proposed adversarial contract design framework accounts for the heterogeneity in honest buyers' demands for data, as well as the presence of adversarial buyers who may purchase data to compromise its privacy. We propose the notion of Price of Adversary (PoAdv) to quantify the effects of adversarial users on the data seller's revenue, and provide bounds on the PoAdv for various classes of adversary utility. We also provide a fast approximate technique to compute contracts in the presence of adversaries.