research-article

Public Access

A theory of pricing private data

Authors:

Daniel Yang Li,

Dan SuciuAuthors Info & Claims

Communications of the ACM, Volume 60, Issue 12

Pages 79 - 86

https://doi.org/10.1145/3139457

Published: 27 November 2017 Publication History

All formats PDF

Abstract

When the analysis of individuals' personal information has value to an institution, but it compromises privacy, should individuals be compensated? We describe the foundations of a market in which those seeking access to data must pay for it and individuals are compensated for the loss of privacy they may suffer.

References

[1]

Acquisti, A., Taylor, C.R., Wagman, L. The economics of privacy. Journal of Economic Literature 52, 2 (2016).

[2]

Balazinska, M., Howe, B., Suciu, D. Data markets in the cloud: An opportunity for the database community. PVLDB 4, 12 (2011), 1482--1485.

[3]

Calo, R. The boundaries of privacy harm. Indiana Law Journal 86, 3 (2011).

[4]

Chen, B.-C., Kifer, D., LeFevre, K., Machanavajjhala, A. Privacy-preserving data publishing. In Foundations and Trends in Databases (2010).

Digital Library

[5]

Dinur, I., Nissim, K. Revealing information while preserving privacy. In PODS (2003), 202--210.

Digital Library

[6]

Dwork, C. A firm foundation for private data analysis. Commun. ACM 54, 1 (2011), 86--95.

Digital Library

[7]

Dwork, C., McSherry, F., Nissim, K., Smith, A. Calibrating noise to sensitivity in private data analysis. In TCC (2006), 265--284.

Digital Library

[8]

Dwork, C., Roth, A. The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science (2014) Now Publishers Inc., Hanover, MA, USA.

Digital Library

[9]

Erlingsson, U., Pihur, V., Korolova, A. Rappor: Randomized aggregatable privacy-preserving ordinal response. In Computer and Communications Security (CCS) (2014), 1054--1067.

Digital Library

[10]

Fleischer, L., Lyu, Y.-H. Approximately optimal auctions for selling privacy when costs are correlated with data. In ACM Conference on Electronic Commerce (2012), 568--585.

Digital Library

[11]

Fung, B.C.M., Wang, K., Chen, R., Yu, P.S. Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surv. 42, 4 (2010).

Digital Library

[12]

Ghosh, A., Roth, A. Selling privacy at auction. In ACM Conference on Electronic Commerce (2011), 199--208.

Digital Library

[13]

Greenberg, A. Apple's "differential privacy" is about collecting your data---but not your data. Wired (Jun 13 2016).

[14]

Halevy, A.Y. Answering queries using views: A survey. VLDB J. 10, 4 (2001), 270--294.

Digital Library

[15]

Hardt, M., Ligett, K., McSherry, F. A simple and practical algorithm for differentially private data release. In NIPS (2012).

Digital Library

[16]

Kifer, D., Abowd, J., Gehrke, J. Vilhuber, L. Privacy: Theory meets practice on the map. In ICDE (2008).

[17]

Koutris, P., Upadhyaya, P., Balazinska, M., Howe, B., Suciu, D. Query-based data pricing. J. ACM 62, 5 (Nov. 2015), 43:1--43:44.

Digital Library

[18]

Laudon, K.C. Markets and privacy. Commun. ACM 39, 9 (1996), 92--104.

Digital Library

[19]

Li, C., Hay, M., Rastogi, V., Miklau, G., McGregor, A. Optimizing linear counting queries under differential privacy. In PODS (2010).

Digital Library

[20]

Li, C., Li, D.Y., Miklau, G. Suciu, D. A theory of pricing private data. ACM Trans. Database Syst. 39, 4 (Dec. 2014), 1--28.

Digital Library

[21]

Li, C., Miklau, G. Pricing aggregate queries in a data marketplace. In WebDB (2012).

[22]

Nash, A., Segoufin, L., Vianu, V. Views and queries: Determinacy and rewriting. TODS 35, 3 (2010).

Digital Library

[23]

Nissim, K., Vadhan, S., Xiao, D. Redrawing the boundaries on purchasing data from privacy-sensitive individuals. In Conference on Innovations in Theoretical Computer Science (2014), 411--422.

Digital Library

Cited By

Ruan YZhang XJoe-Wong C(2024)How Valuable is Your Data? Optimizing Client Recruitment in Federated LearningIEEE/ACM Transactions on Networking10.1109/TNET.2024.342226432:5(4207-4221)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3422264
He ZCai Z(2024)Trading Aggregate Statistics Over Private Internet of Things DataIEEE Transactions on Computers10.1109/TC.2023.333181673:2(394-407)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TC.2023.3331816
Liu YZheng ZWu FChen G(2024)When Crowdsourcing Meets Data Markets: A Fair Data Value Metric for Data TradingJournal of Computer Science and Technology10.1007/s11390-023-2519-039:3(671-690)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11390-023-2519-0
Show More Cited By

Index Terms

A theory of pricing private data
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

Recommendations

A Theory of Pricing Private Data
Invited Articles Issue, SIGMOD 2013, PODS 2013 and ICDT 2013

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'...
A theory of pricing private data
ICDT '13: Proceedings of the 16th International Conference on Database Theory

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 ...
Differentially Private Contextual Dynamic Pricing
AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems

In this paper, we design differentially private algorithms for the contextual dynamic pricing problem. In contextual dynamic pricing, the seller sells heterogeneous products to buyers that arrive sequentially. At each time step, a buyer arrives with ...

Comments

Information & Contributors

Information

Published In

cover image Communications of the ACM

Communications of the ACM Volume 60, Issue 12

December 2017

91 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3167461

Editor:
Andrew A. Chien
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Copyright © 2017 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 November 2017

Published in CACM Volume 60, Issue 12

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Science Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

44
Total Citations
View Citations
18,547
Total Downloads

Downloads (Last 12 months)401
Downloads (Last 6 weeks)51

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ruan YZhang XJoe-Wong C(2024)How Valuable is Your Data? Optimizing Client Recruitment in Federated LearningIEEE/ACM Transactions on Networking10.1109/TNET.2024.342226432:5(4207-4221)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3422264
He ZCai Z(2024)Trading Aggregate Statistics Over Private Internet of Things DataIEEE Transactions on Computers10.1109/TC.2023.333181673:2(394-407)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TC.2023.3331816
Liu YZheng ZWu FChen G(2024)When Crowdsourcing Meets Data Markets: A Fair Data Value Metric for Data TradingJournal of Computer Science and Technology10.1007/s11390-023-2519-039:3(671-690)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11390-023-2519-0
Ke TSudhir K(2023)Privacy Rights and Data SecurityManagement Science10.1287/mnsc.2022.461469:8(4389-4412)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1287/mnsc.2022.4614
Gangopadhyay SChakraborty SDas S(2023)Data Trading in Smart Grids: Future Research Directions2023 IEEE Texas Power and Energy Conference (TPEC)10.1109/TPEC56611.2023.10078634(1-6)Online publication date: 13-Feb-2023
https://doi.org/10.1109/TPEC56611.2023.10078634
Cai HYang YFan WXiao FZhu Y(2023)Towards Correlated Data Trading for High-Dimensional Private DataIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.323769134:3(1047-1059)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TPDS.2023.3237691
Liu WZhang XDuan JJoe-Wong CZhou ZChen X(2023)DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning With Static and Streaming DatasetsIEEE Transactions on Mobile Computing10.1109/TMC.2023.333701623:7(7664-7679)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1109/TMC.2023.3337016
Cai HYe FYang YXiao FZhu Y(2023)Toward Correlated Data Trading for Private Web Browsing HistoryIEEE Internet of Things Journal10.1109/JIOT.2023.323770710:7(5859-5872)Online publication date: 1-Apr-2023
https://doi.org/10.1109/JIOT.2023.3237707
Liu WZhang XDuan JJoe-Wong CZhou ZChen X(2023)AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188807(1-10)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWQoS57198.2023.10188807
Zhang JXia HSun QLiu JXiong LPei JRen K(2023)Dynamic Shapley Value Computation2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00055(639-652)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00055
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Figures

Tables

Media

View Issue’s Table of Contents