research-article

Measuring risk and utility of anonymized data using information theory

Authors:

Josep Domingo-Ferrer,

David Rebollo-MonederoAuthors Info & Claims

EDBT/ICDT '09: Proceedings of the 2009 EDBT/ICDT Workshops

Pages 126 - 130

https://doi.org/10.1145/1698790.1698811

Published: 22 March 2009 Publication History

Abstract

Before releasing anonymized microdata (individual data) it is essential to evaluate whether: i) their utility is high enough for their release to make sense; ii) the risk that the anonymized data result in disclosure of respondent identity or respondent attribute values is low enough. Utility and disclosure risk measures are used for the above evaluation, which normally lack a common theoretical framework allowing to trade off utility and risk in a consistent way. We explore in this paper the use of information-theoretic measures based on the notion of mutual information.

References

[1]

J. M. Abowd and L. Vilhuber. How protective are synthetic data? In J. Domingo-Ferrer and Y. Saygin, editors, Privacy in Statistical Databases, volume 5262 of Lecture Notes in Computer Science, pages 239--246, Berlin Heidelberg, 2008. Springer.

Digital Library

[2]

L. M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Comput. Math., Math. Phys., 7:200--217, 1967.

[3]

J. Brickell and V. Shmatikov. The cost of privacy: destruction of data-mining utility in anonymized data publishing. In Proc. of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 70--78, 2008.

Digital Library

[4]

J. Domingo-Ferrer and V. Torra. Disclosure protection methods and information loss for microdata. In P. Doyle, J. I. Lane, J. J. M. Theeuwes, and L. Zayatz, editors, Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, pages 91--110, Amsterdam, 2001. North-Holland.

[5]

V. S. Iyengar. Transforming data to satisfy privacy constraints. In Proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 279--288, 2002.

Digital Library

[6]

J. Lane, P. Heus and T. Mulcahy. Data access in a cyber world: making use of cyberinfrastructure. Transactions on Data Privacy, 1(1): 2--16, 2008.

Digital Library

[7]

N. Li, T. Li, and S. Venkatasubramanian. t-closeness: privacy beyond k-anonymity and l-diversity. In Proceedings of the IEEE ICDE 2007, 2007.

[8]

W. Li. Mutual information functions vs correlation functions. Journal of Statistical Physics, 60:823--837, 1990.

[9]

J. M. Mateo-Sanz, J. Domingo-Ferrer, and F. Sebé. Probabilistic information loss measures in confidentiality protection of continuous microdata. Data Mining and Knowledge Discovery, 11(2), 2005. 181--193.

Digital Library

[10]

D. Rebollo-Monedero. Quantization and Transforms for Distributed Source Coding. PhD thesis, Stanford University, 2007.

[11]

D. Rebollo-Monedero, J. Forné, and J. Domingo-Ferrer. From t-closeness to pram and noise addition via information theory. In Privacy in Statistical Databases-PSD 2008, volume 5262 of Lecture Notes in Computer Science, pages 100--112, Berlin Heidelberg, 2008.

Digital Library

[12]

D. Rebollo-Monedero, S. Rane, A. Aaron, and B. Girod. High-rate quantization and transform coding with side information at the decoder. Signal Processing, 86(11):3160--3179, 2006.

Digital Library

[13]

P. Samarati. Protecting respondents' identities in microdata release. IEEE Transactions on Knowledge and Data Engineering, 13(6):1010--1027, 2001.

Digital Library

[14]

M. Trottini. Decision models for data disclosure limitation. PhD thesis, Carnegie Mellon University, 2003. http://www.niss.org/dgii/TR/Thesis-Trottini-final.pdf.

[15]

J. Xu, W. Wang, J. Pei, X. Wang, B. Shi, and A. W.-C. Fu. Utility-based anonymization using local recoding. In Proc. of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785--790, 2006.

Digital Library

Cited By

Vranopoulos GClarke NAtkinson S(2023)Big Data Confidentiality: An Approach Toward Corporate Compliance Using a Rule-Based SystemBig Data10.1089/big.2022.0201Online publication date: 31-Oct-2023
https://doi.org/10.1089/big.2022.0201
Sadhya DChakraborty B(2022)Quantifying the Effects of Anonymization Techniques Over Micro-DatabasesIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2022.314175410:4(1979-1992)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TETC.2022.3141754
Kiennert CDe Vos NKnockaert MGarcia-Alfaro J(2019)The Influence of Conception Paradigms on Data Protection in E-Learning Platforms: A Case StudyIEEE Access10.1109/ACCESS.2019.29152757(64110-64119)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2915275
Show More Cited By

Index Terms

Measuring risk and utility of anonymized data using information theory

Recommendations

An information theoretic privacy and utility measure for data sanitization mechanisms
CODASPY '12: Proceedings of the second ACM conference on Data and Application Security and Privacy

Data collection agencies publish sensitive data for legitimate purposes, such as research, marketing and etc. Data publishing has attracted much interest in research community due to the important concerns over the protection of individuals privacy. As ...
Injecting utility into anonymized datasets
SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data

Limiting disclosure in data publishing requires a careful balance between privacy and utility. Information about individuals must not be revealed, but a dataset should still be useful for studying the characteristics of a population. Privacy ...
Information based data anonymization for classification utility

Anonymization is a practical approach to protect privacy in data. The major objective of privacy preserving data publishing is to protect private information in data whereas data is still useful for some intended applications, such as building ...

Comments

comments powered by Disqus.

Information & Contributors

Information

Published In

EDBT/ICDT '09: Proceedings of the 2009 EDBT/ICDT Workshops

March 2009

218 pages

ISBN:9781605586502

DOI:10.1145/1698790

Copyright © 2009 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: 22 March 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Spanish Government

Conference

EDBT/ICDT '09

EDBT/ICDT '09: EDBT/ICDT '09 joint conference

March 22, 2009

Saint-Petersburg, Russia

Acceptance Rates

Overall Acceptance Rate 7 of 10 submissions, 70%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
254
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)3

Reflects downloads up to 20 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Vranopoulos GClarke NAtkinson S(2023)Big Data Confidentiality: An Approach Toward Corporate Compliance Using a Rule-Based SystemBig Data10.1089/big.2022.0201Online publication date: 31-Oct-2023
https://doi.org/10.1089/big.2022.0201
Sadhya DChakraborty B(2022)Quantifying the Effects of Anonymization Techniques Over Micro-DatabasesIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2022.314175410:4(1979-1992)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TETC.2022.3141754
Kiennert CDe Vos NKnockaert MGarcia-Alfaro J(2019)The Influence of Conception Paradigms on Data Protection in E-Learning Platforms: A Case StudyIEEE Access10.1109/ACCESS.2019.29152757(64110-64119)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2915275
Miche YOliver IRen WHoltmanns SAkusok ALendasse A(2017)Practical Estimation of Mutual Information on Non-Euclidean SpacesMachine Learning and Knowledge Extraction10.1007/978-3-319-66808-6_9(123-136)Online publication date: 24-Aug-2017
https://doi.org/10.1007/978-3-319-66808-6_9
Chen YElliot MSakshaug J(2016)A Genetic Algorithm Approach to Synthetic Data ProductionProceedings of the 1st International Workshop on AI for Privacy and Security10.1145/2970030.2970034(1-4)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.1145/2970030.2970034
Almasi MSiddiqui TMohammed NHemmati H(2016)The Risk-Utility Tradeoff for Data Privacy Models2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS)10.1109/NTMS.2016.7792481(1-5)Online publication date: Nov-2016
https://doi.org/10.1109/NTMS.2016.7792481
Mortazavi RJalili S(2016)Enhancing aggregation phase of microaggregation methods for interval disclosure risk minimizationData Mining and Knowledge Discovery10.1007/s10618-015-0432-z30:3(605-639)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1007/s10618-015-0432-z
Grechanik MMcMillan CDasgupta TPoshyvanyk DGethers MRoy CBegel AMoonen L(2014)Redacting sensitive information in software artifactsProceedings of the 22nd International Conference on Program Comprehension10.1145/2597008.2597138(314-325)Online publication date: 2-Jun-2014
https://dl.acm.org/doi/10.1145/2597008.2597138
Abril DNavarro-Arribas GTorra V(2014)Data Privacy with $$R$$Advanced Research in Data Privacy10.1007/978-3-319-09885-2_5(63-82)Online publication date: 22-Aug-2014
https://doi.org/10.1007/978-3-319-09885-2_5
Navarro-Arribas GTorra V(2012)Information fusion in data privacyInformation Fusion10.1016/j.inffus.2012.01.00113:4(235-244)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1016/j.inffus.2012.01.001
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.

Figures

Tables

Media

View Table of Conten

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.

Alternative Proxies:

Alternative Proxy