research-article

A Theory of Pricing Private Data

Authors:

Daniel Yang Li,

Dan SuciuAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 39, Issue 4

Article No.: 34, Pages 1 - 28

https://doi.org/10.1145/2691190.2691191

Published: 30 December 2014 Publication History

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 the 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 awareness of the value of 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 article 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 pricing function and micropayments, and characterize valid solutions.

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Index Terms

A Theory of Pricing Private Data
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 39, Issue 4

Invited Articles Issue, SIGMOD 2013, PODS 2013 and ICDT 2013

December 2014

341 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/2691190

Editor:
Christian S. Jensen
Aalborg University, Denmark

Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 30 December 2014

Accepted: 01 August 2014

Revised: 01 August 2014

Received: 01 October 2013

Published in TODS Volume 39, Issue 4

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Funding Sources

NSF CNS-0964094
European Research Council under the Webdam grant
NSF CNS-1012748
NSF IIS-0915054 and NSF CCF-1047815

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Cited By

TAO XPENG JWANG YWANG YHU CLU Z(2025)Information metrics for data components based on information entropy: data pricing and its application analysis for electric energy statisticsSCIENTIA SINICA Informationis10.1360/SSI-2024-016955:3(654)Online publication date: 19-Feb-2025
https://doi.org/10.1360/SSI-2024-0169
Majumdar RGurtoo AMaileckal M(2025)Developing a data pricing framework for data exchangeFuture Business Journal10.1186/s43093-025-00422-z11:1Online publication date: 20-Jan-2025
https://doi.org/10.1186/s43093-025-00422-z
Shen BMa QYang GWang RJi W(2025)A Stackelberg Evolutionary Game Theoretic Framework for Dynamical Data Trading in Artificial Intelligence of ThingsIEEE Internet of Things Journal10.1109/JIOT.2024.348635112:5(5360-5372)Online publication date: 1-Mar-2025
https://doi.org/10.1109/JIOT.2024.3486351
Ge YQin JZheng SZhong YTang BQiu YMao RYuan YOnizuka MXiao C(2024)Privacy-Enhanced Database Synthesis for Benchmark PublishingProceedings of the VLDB Endowment10.14778/3705829.370585518:2(413-425)Online publication date: 1-Oct-2024
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Ke WLiu YWang JFang ZChi ZGuo YWang RWang P(2024)DecentralDC: Assessing data contribution under decentralized sharing and exchange blockchainPLOS ONE10.1371/journal.pone.031074719:10(e0310747)Online publication date: 24-Oct-2024
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Luo XPei JXu CZhang WXu J(2024)Fast Shapley Value Computation in Data Assemblage Tasks as Cooperative Simple GamesProceedings of the ACM on Management of Data10.1145/36393112:1(1-28)Online publication date: 26-Mar-2024
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