Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2019

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 as a mechanism design problem, specifically using a contract-theoretic approach. Our proposed adversarial contract design framework provides a fundamental extension to the classic contract theory set-up in order to account 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.

Keywords

contract theory, pricing private data, data commercialization

Discipline

Data Storage Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 20th ACM Conference on Economics and Computation (EC-19), Phoenix, Arizona, USA, 2019 June 24–28

First Page

681

Last Page

699

Identifier

10.1145/3328526.3329633

Publisher

ACM

City or Country

Phoenix, Arizona, USA

Additional URL

https://doi.org/10.1145/3328526.3329633

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