research-article

Unlocking the Value of Privacy: Trading Aggregate Statistics over Private Correlated Data

Authors:

Guihai ChenAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2031 - 2040

https://doi.org/10.1145/3219819.3220013

Published: 19 July 2018 Publication History

Abstract

With the commoditization of personal privacy, pricing private data has become an intriguing problem. In this paper, we study noisy aggregate statistics trading from the perspective of a data broker in data markets. We thus propose ERATO, which enables aggrEgate statistics pRicing over privATe cOrrelated data. On one hand, ERATO guarantees arbitrage freeness against cunning data consumers. On the other hand, ERATO compensates data owners for their privacy losses using both bottom-up and top-down designs. We further apply ERATO to three practical aggregate statistics, namely weighted sum, probability distribution fitting, and degree distribution, and extensively evaluate their performances on MovieLens dataset, 2009 RECS dataset, and two SNAP large social network datasets, respectively. Our analysis and evaluation results reveal that ERATO well balances utility and privacy, achieves arbitrage freeness, and compensates data owners more fairly than differential privacy based approaches.

Supplementary Material

suppl.mov (r0518p.mp4)

Supplemental video

Download
50.35 MB

References

[1]

2009 RECS Dataset. 2013. https://www.eia.gov/consumption/ residential/data/2009/index.php?view=microdata.

[2]

Joshua Brustein. 2012. Start-Ups Seek to Help Users Put a Price on Their Personal Data. The New York Times (Feb. 2012).

[3]

Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In KDD. 1082--1090.

Digital Library

[4]

Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, and Juba Ziani. 2015. Accuracy for Sale: Aggregating Data with a Variance Constraint. In ITCS. 317--324.

Digital Library

[5]

Data Brokers: A Call For Transparency and Accountability. 2014. https://www.ftc.gov/reports/data-brokers-call-transparency% 2Daccountability-report-federal-trade-commission-may-2014.

[6]

Shaleen Deep and Paraschos Koutris. 2017. QIRANA: A Framework for Scalable Query Pricing. In SIGMOD. 699--713.

Digital Library

[7]

Cynthia Dwork and Aaron Roth. 2014. The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science 9, 3--4 (2014), 211--407.

Digital Library

[8]

Nicole Eikmeier and David F. Gleich. 2017. Revisiting Power-law Distributions in Spectra of Real World Networks. In KDD.

Digital Library

[9]

Facebook Is Not the Problem. Lax Privacy Rules Are. 2018. https://www.nytimes.com/2018/04/01/opinion/facebooklax- privacy-rules.html.

[10]

Arpita Ghosh and Aaron Roth. 2011. Selling privacy at auction. In EC. 199--208.

Digital Library

[11]

Daniel Kifer and Ashwin Machanavajjhala. 2011. No free lunch in data privacy. In SIGMOD. 193--204.

Digital Library

[12]

Daniel Kifer and Ashwin Machanavajjhala. 2012. A rigorous and customizable framework for privacy. In PODS. 77--88.

Digital Library

[13]

Paraschos Koutris, Prasang Upadhyaya, Magdalena Balazinska, Bill Howe, and Dan Suciu. 2013. Toward practical query pricing with QueryMarket. In SIGMOD. 613--624.

Digital Library

[14]

Chao Li, Daniel Yang Li, Gerome Miklau, and Dan Suciu. 2017. A theory of pricing private data. Communications of the ACM 60, 12 (2017), 79--86.

Digital Library

[15]

Bing-Rong Lin and Daniel Kifer. 2014. On Arbitrage-free Pricing for General Data Queries. PVLDB 7, 9 (2014), 757--768.

Digital Library

[16]

Changchang Liu, Supriyo Chakraborty, and Prateek Mittal. 2016. Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples. In NDSS.

[17]

MovieLens 1M Dataset. 2003. https://grouplens.org/datasets/ movielens/1m/.

[18]

Chaoyue Niu, Zhenzhe Zheng, Fan Wu, Xiaofeng Gao, and Guihai Chen. 2018. Achieving Data Truthfulness and Privacy Preservation in Data Markets. IEEE Transactions on Knowledge and Data Engineering (2018).

[19]

Chaoyue Niu, Zhenzhe Zheng, Fan Wu, Shaojie Tang, Xiaofeng Gao, and Guihai Chen. 2018. Unlocking the Value of Privacy: Trading Aggregate Statistics over Private Correlated Data. https: //www.dropbox.com/s/gp86elkmraitcf9/.

[20]

Personal Data: The Emergence of a New Asset Class. 2011. https://www.weforum.org/reports/ personal-data-emergence-new-asset-class.

[21]

Raluca A. Popa, Andrew J. Blumberg, Hari Balakrishnan, and Frank H. Li. 2011. Privacy and accountability for location-based aggregate statistics. In CCS. 653--666.

Digital Library

[22]

Aaron Roth. 2017. Technical Perspective: Pricing Information (and Its Implications). Communications of the ACM 60, 12 (2017), 78.

Digital Library

[23]

Elaine Shi, T-H. Hubert Chan, Eleanor Rieffel, Richard Chow, and Dawn Song. 2011. Privacy-Preserving Aggregation of Time-Series Data. In NDSS.

[24]

SNAP Datasets: Stanford Large Network Dataset Collection. 2014. http://snap.stanford.edu/data.

[25]

Shuang Song, Yizhen Wang, and Kamalika Chaudhuri. 2017. Pufferfish Privacy Mechanisms for Correlated Data. In SIGMOD.

Digital Library

[26]

The data brokers: Selling your personal information. 2014. https://www.cbsnews.com/news/ data-brokers-selling-personal-information-60-minutes/.

[27]

Twitter Sold Data Access to Cambridge Analytica-Linked Researcher. 2018. https://www.bloomberg.com/news/articles/2018- 04--29/twitter-sold-cambridge-analytica-researcher-public-dataaccess/.

[28]

Ali Vanderveld, Addhyan Pandey, Angela Han, and Rajesh Parekh. 2016. An Engagement-Based Customer Lifetime Value System for E-commerce. In KDD. 293--302.

Digital Library

[29]

Bin Yang, Issei Sato, and Hiroshi Nakagawa. 2015. Bayesian Differential Privacy on Correlated Data. In SIGMOD. 747--762.

Digital Library

Cited By

Si MPei J(2024)Counterfactual Explanation of Shapley Value in Data CoalitionsProceedings of the VLDB Endowment10.14778/3681954.368200417:11(3332-3345)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682004
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
https://doi.org/10.1371/journal.pone.0310747
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
https://dl.acm.org/doi/10.1145/3639311
Show More Cited By

Index Terms

Unlocking the Value of Privacy: Trading Aggregate Statistics over Private Correlated Data
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Economics of security and privacy

Recommendations

On the Evaluation of the Privacy Breach in Disassociated Set-valued Datasets
ICETE 2016: Proceedings of the 13th International Joint Conference on e-Business and Telecommunications

Data anonymization is gaining much attention these days as it provides the fundamental requirements to safely

outsource datasets containing identifying information. While some techniques add noise to protect privacy

others use generalization to hide the ...
t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservation
Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate k-anonymous data sets, where the identity of ...
Privacy-Preserving Mechanisms for Heterogeneous Data Types
SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

Due to the pervasiveness of Interconnected devices, large amounts of heterogeneous data types are being continuously collected. Regardless of the benefits that come from sharing data, exposing sensitive and private information arises serious privacy ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Ministry of Science and Technology of the People's Republic of China
Science and Technology Commission of Shanghai Municipality
CCF-Tencent Open Research Fund
National Natural Science Foundation of China

Conference

KDD '18

Sponsor:

KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

Acceptance Rates

KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

50
Total Citations
View Citations
1,247
Total Downloads

Downloads (Last 12 months)86
Downloads (Last 6 weeks)4

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Si MPei J(2024)Counterfactual Explanation of Shapley Value in Data CoalitionsProceedings of the VLDB Endowment10.14778/3681954.368200417:11(3332-3345)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682004
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
https://doi.org/10.1371/journal.pone.0310747
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
https://dl.acm.org/doi/10.1145/3639311
Yang FLiao XLei XMu NZhang D(2024)Towards Privacy-Preserving and Practical Data Trading for Aggregate StatisticIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33311799:3(452-463)Online publication date: May-2024
https://doi.org/10.1109/TSUSC.2023.3331179
Sun PLiao GChen XHuang J(2024)A Socially Optimal Data Marketplace With Differentially Private Federated LearningIEEE/ACM Transactions on Networking10.1109/TNET.2024.335186432:3(2221-2236)Online publication date: Jun-2024
https://doi.org/10.1109/TNET.2024.3351864
Zhang MLi XRen YLuo BMiao YLiu XDeng R(2024)Privacy-Preserved Data Trading Via Verifiable Data DisturbanceIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.332366921:4(3126-3140)Online publication date: Jul-2024
https://doi.org/10.1109/TDSC.2023.3323669
Zhang MPei J(2024)Protecting Data Buyer Privacy in Data MarketsIEEE Internet Computing10.1109/MIC.2024.339862628:4(14-20)Online publication date: Jul-2024
https://doi.org/10.1109/MIC.2024.3398626
Li JNi YZhang JYu JHe Y(2024)A Contract-Based Privacy-Preserving Longitudinal Data Trading Mechanism for IoTIEEE Internet of Things Journal10.1109/JIOT.2024.345613411:24(40897-40908)Online publication date: 15-Dec-2024
https://doi.org/10.1109/JIOT.2024.3456134
Fu YMiao XPeng HNa CDeng SYin J(2024)Online Query-Based Data Pricing with Time-Discounting Valuations2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00266(3449-3461)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00266
Ding YTian Y(2024)Research on Pricing of Data Based on Bi-level Programming ModelAnnals of Data Science10.1007/s40745-024-00549-wOnline publication date: 16-Jun-2024
https://doi.org/10.1007/s40745-024-00549-w
Show More Cited By

View Options

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents