research-article

Differential identifiability

Authors:

Chris CliftonAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1041 - 1049

https://doi.org/10.1145/2339530.2339695

Published: 12 August 2012 Publication History

Abstract

A key challenge in privacy-preserving data mining is ensuring that a data mining result does not inherently violate privacy. ε-Differential Privacy appears to provide a solution to this problem. However, there are no clear guidelines on how to set ε to satisfy a privacy policy. We give an alternate formulation, Differential Identifiability, parameterized by the probability of individual identification. This provides the strong privacy guarantees of differential privacy, while letting policy makers set parameters based on the established privacy concept of individual identifiability.

Supplementary Material

JPG File (307_t_talk_10.jpg)

Download
16.02 KB

MP4 File (307_t_talk_10.mp4)

Download
408.28 MB

References

[1]

C. C. Aggarwal and P. S. Yu, Eds., Privacy-Preserving Data Mining: Models and Algorithms, ser. Advances in Database Systems. Springer, 2008, vol. 34.

Digital Library

[2]

R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta, "Discovering frequent patterns in sensitive data," in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'10). New York, NY, USA: ACM, 2010, pp. 503--512. http://doi.acm.org/10.1145/1835804.1835869.

Digital Library

[3]

K. Chaudhuri and C. Monteleoni, "Privacy-preserving logistic regression," in Proceeding of the 22nd Annual Conference on Neural Information Processing Systems (NIPS), 2008, pp. 289--296.

[4]

G. Cormode, "Personal privacy vs population privacy: learning to attack anonymization," in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'11). New York, NY, USA: ACM, 2011, pp. 1253--1261. http://doi.acm.org/10.1145/2020408.2020598.

Digital Library

[5]

B. Ding, M. Winslett, J. Han, and Z. Li, "Differentially private data cubes: optimizing noise sources and consistency," in Proceedings of the 2011 international conference on Management of data (SIGMOD'11). New York, NY, USA: ACM, 2011, pp. 217--228. http://doi.acm.org/10.1145/1989323.1989347.

Digital Library

[6]

C. Dwork, F. McSherry, K. Nissim, and A. Smith, "Calibrating noise to sensitivity in private data analysis," in Proc. of the 3rd Theory of Cryptography Conference. Springer, 2006, pp. 265--284.

Digital Library

[7]

C. Dwork, "Differential privacy," in 33rd International Colloquium on Automata, Languages and Programming (ICALP 2006), Venice, Italy, Jul. 9-16 2006, pp. 1--12. http://dx.doi.org/10.1007/11787006 1.

Digital Library

[8]

"Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data," Official Journal of the European Communities, vol. No I., no. 281, pp. 31--50, Oct. 24 1995. http://ec.europa.eu/justice_home/fsj/privacy/law/index_en.htm.

[9]

A. Frank and A. Asuncion, "UCI machine learning repository," 2010. http://archive.ics.uci.edu/ml.

[10]

A. Friedman and A. Schuster, "Data mining with differential privacy," in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'11). New York, NY, USA: ACM, 2010, pp. 493--502. http://doi.acm.org/10.1145/1835804.1835868.

Digital Library

[11]

"Standard for privacy of individually identifiable health information," Federal Register, vol. 67, no. 157, pp. 53-181--53-273, Aug. 14 2002. http://www.hhs.gov/ocr/privacy/hipaa/administrative/privacyrule/index.html.

[12]

D. Kifer and A. Machanavajjhala, "No free lunch in data privacy," in Proceedings of the 2011 Intl. Conf. on Management of data. 2011, pp. 193--204.

Digital Library

[13]

J. Lee and C. Clifton, "How much is enough? choosing ε for differential privacy," in Information Security, ser. Lecture Notes in Computer Science, X. Lai, J. Zhou, and H. Li, Eds. Springer Berlin / Heidelberg, 2011, vol. 7001, pp. 325--340.

Digital Library

[14]

A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, "l-diversity: Privacy beyond k-anonymity,"ACM Trans. on Knowledge Discovery from Data (TKDD), vol. 1, no. 1, pp. 3--es, 2007.

Digital Library

[15]

F. McSherry, "Privacy integrated queries: an extensible platform for privacy-preserving data analysis," Commun. ACM, vol. 53, pp. 89--97, Sep. 2010. http://doi.acm.org/10.1145/1810891.1810916.

Digital Library

[16]

F. McSherry and I. Mironov, "Differentially-private recommender systems: Building privacy into the netflix prize contenders," in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, Jun. 28-Jul. 1 2009.

Digital Library

[17]

N. Mohammed, R. Chen, B. C. Fung, and P. S. Yu, "Differentially private data release for data mining," in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'11). ACM, 2011, pp. 493--501. http://doi.acm.org/10.1145/2020408.2020487.

Digital Library

[18]

M. E. Nergiz, M. Atzori, and C. Clifton, "Hiding the presence of individuals from shared databases," in Proceedings of the 2007 ACM SIGMOD international conference on Management of data. 2007, pp. 665--676. http://doi.acm.org/10.1145/1247480.1247554.

Digital Library

[19]

P. Samarati, "Protecting respondents' identities in microdata release," IEEE Trans. on Knowl. and Data Eng., vol. 13, pp. 1010--1027, Nov. 2001. http://dx.doi.org/10.1109/69.971193.

Digital Library

[20]

L. Sweeney, "k-anonymity: a model for protecting privacy," Int. J. Uncertain. Fuzziness Knowl.-Based Syst., vol. 10, pp. 557--570, Oct. 2002. http://dl.acm.org/citation.cfm?id=774544.774552.

Digital Library

[21]

J. Vaidya, C. Clifton, and M. Zhu, Privacy Preserving Data Mining, ser. Advances in Information Security. Springer, 2006, vol. 19. http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-0-387-25886-7.

[22]

X. Xiao, G. Wang, and J. Gehrke, "Differential privacy via wavelet transforms," IEEE Trans. on Knowledge and Data Engineering, vol. 23, no. 8, pp. 1200--1214, Aug. 2011.

Digital Library

[23]

N. Zhang, M. Li, and W. Lou, "Distributed data mining with differential privacy," in 2011 IEEE International Conference on Communications (ICC), Jun. 2011, pp. 1--5.

Cited By

Nergiz MAtzori MClifton C(2025)δ-PresenceEncyclopedia of Cryptography, Security and Privacy10.1007/978-3-030-71522-9_1570(1858-1861)Online publication date: 8-Jan-2025
https://doi.org/10.1007/978-3-030-71522-9_1570
Liu QHuang YJin CZhou XMao YCatal CCheng L(2024)Privacy and Integrity Protection for IoT Multimodal Data Using Machine Learning and BlockchainACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363876920:6(1-18)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3638769
Tripathi KBiswas SKhare NShukla S(2024)Tackling Privacy Concerns in Correlated Big Data: A Comprehensive Review with Machine Learning Insights2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)10.1109/SCEECS61402.2024.10482215(1-6)Online publication date: 24-Feb-2024
https://doi.org/10.1109/SCEECS61402.2024.10482215
Show More Cited By

Index Terms

Differential identifiability

Recommendations

A Novel Differential Privacy Approach that Enhances Classification Accuracy
C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

In the recent past, there has been a tremendous increase of large repositories of data, examples being in healthcare data, consumer data from retailers, and airline passenger data. These data are continually being shared with interested parties, either ...
An Emerging Strategy for Privacy Preserving Databases: Differential Privacy
HCI for Cybersecurity, Privacy and Trust
Abstract
Data De-identification and Differential Privacy are two possible approaches for providing data security and user privacy. Data de-identification is the process where the personal identifiable information of individuals is extracted to create ...
Applying Differential Privacy to Matrix Factorization
RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Recommender systems are increasingly becoming an integral part of on-line services. As the recommendations rely on personal user information, there is an inherent loss of privacy resulting from the use of such systems. While several works studied ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Copyright © 2012 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: 12 August 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '12

Sponsor:

KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

Acceptance Rates

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

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

75
Total Citations
View Citations
1,174
Total Downloads

Downloads (Last 12 months)45
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nergiz MAtzori MClifton C(2025)δ-PresenceEncyclopedia of Cryptography, Security and Privacy10.1007/978-3-030-71522-9_1570(1858-1861)Online publication date: 8-Jan-2025
https://doi.org/10.1007/978-3-030-71522-9_1570
Liu QHuang YJin CZhou XMao YCatal CCheng L(2024)Privacy and Integrity Protection for IoT Multimodal Data Using Machine Learning and BlockchainACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363876920:6(1-18)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3638769
Tripathi KBiswas SKhare NShukla S(2024)Tackling Privacy Concerns in Correlated Big Data: A Comprehensive Review with Machine Learning Insights2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)10.1109/SCEECS61402.2024.10482215(1-6)Online publication date: 24-Feb-2024
https://doi.org/10.1109/SCEECS61402.2024.10482215
Allmann JNuñez von Voigt STschorsch F(2024)An applied Perspective: Estimating the Differential Identifiability Risk of an Exemplary SOEP Data Set2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW61312.2024.00013(48-55)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSPW61312.2024.00013
Ha TDang T(2024)Navigating Differential Privacy Constraints in Machine LearningFuture Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications10.1007/978-981-96-0437-1_2(16-30)Online publication date: 27-Nov-2024
https://doi.org/10.1007/978-981-96-0437-1_2
Saeidian SCervia GOechtering TSkoglund M(2023)Pointwise Maximal LeakageIEEE Transactions on Information Theory10.1109/TIT.2023.330437869:12(8054-8080)Online publication date: Dec-2023
https://doi.org/10.1109/TIT.2023.3304378
Zhou ZXu CWang MKuang XZhuang YYu S(2023)A Multi-Shuffler Framework to Establish Mutual Confidence for Secure Federated LearningIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.321557420:5(4230-4244)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TDSC.2022.3215574
Vu XTran SJiang L(2023)dpUGC: Learn Differentially Private Representation for User Generated Contents (Best Paper Award, Third Place, Shared)Computational Linguistics and Intelligent Text Processing10.1007/978-3-031-24337-0_23(316-331)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24337-0_23
Karegar FAlaqra AFischer-Hübner SChiasson SKapadia A(2022)Exploring user-suitable metaphors for differentially private data analysesProceedings of the Eighteenth USENIX Conference on Usable Privacy and Security10.5555/3563609.3563619(175-193)Online publication date: 8-Aug-2022
https://dl.acm.org/doi/10.5555/3563609.3563619
Wunderlich DBernau DAldà FParra-Arnau JStrufe T(2022)On the Privacy–Utility Trade-Off in Differentially Private Hierarchical Text ClassificationApplied Sciences10.3390/app12211117712:21(11177)Online publication date: 4-Nov-2022
https://doi.org/10.3390/app122111177
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