poster

Anonymizing data with quasi-sensitive attribute values

Authors:

Pu Shi,

Li Xiong,

Benjamin C.M. FungAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 1389 - 1392

https://doi.org/10.1145/1871437.1871628

Published: 26 October 2010 Publication History

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Abstract

We study the problem of anonymizing data with quasi-sensitive attributes. Quasi-sensitive attributes are not sensitive by themselves, but certain values or their combinations may be linked to external knowledge to reveal indirect sensitive information of an individual. We formalize the notion of l-diversity and t-closeness for quasi-sensitive attributes, which we call QS l-diversity and QS t-closeness, to prevent indirect sensitive attribute disclosure. We propose a two-phase anonymization algorithm that combines quasi-identifying value generalization and quasi-sensitive value suppression to achieve QS l-diversity and QS t-closeness.

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Cited By

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Lu XSong W(2024)Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles DatasetsApplied Sciences10.3390/app1408323014:8(3230)Online publication date: 11-Apr-2024
https://doi.org/10.3390/app14083230
Yaji SBayyapu N(2023)Reinforcement Technique for Classifying Quasi and Non-quasi Attributes for Privacy Preservation and Data ProtectionApplications and Techniques in Information Security10.1007/978-981-99-2264-2_1(3-17)Online publication date: 12-May-2023
https://doi.org/10.1007/978-981-99-2264-2_1
Li YYuan SYuan YChen CYu J(2022)Anonymization of Quasi-Sensitive Attribute Sets in Aggregated DatasetSecurity and Communication Networks10.1155/2022/97218172022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9721817
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Index Terms

Anonymizing data with quasi-sensitive attribute values
1. Security and privacy
  1. Database and storage security
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Theory of database privacy and security

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Information & Contributors

Information

Published In

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

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

New York, NY, United States

Publication History

Published: 26 October 2010

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CIKM '10: International Conference on Information and Knowledge Management

October 26 - 30, 2010

ON, Toronto, Canada

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Lu XSong W(2024)Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles DatasetsApplied Sciences10.3390/app1408323014:8(3230)Online publication date: 11-Apr-2024
https://doi.org/10.3390/app14083230
Yaji SBayyapu N(2023)Reinforcement Technique for Classifying Quasi and Non-quasi Attributes for Privacy Preservation and Data ProtectionApplications and Techniques in Information Security10.1007/978-981-99-2264-2_1(3-17)Online publication date: 12-May-2023
https://doi.org/10.1007/978-981-99-2264-2_1
Li YYuan SYuan YChen CYu J(2022)Anonymization of Quasi-Sensitive Attribute Sets in Aggregated DatasetSecurity and Communication Networks10.1155/2022/97218172022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9721817
Husnoo MAnwar AChakrabortty RDoss RRyan M(2021)Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2021.31243099(153276-153304)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3124309
Hassan MRehmani MChen J(2020)Differential Privacy Techniques for Cyber Physical Systems: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2019.294474822:1(746-789)Online publication date: Sep-2021
https://doi.org/10.1109/COMST.2019.2944748
Sei YOkumura HTakenouchi TOhsuga A(2019)Anonymization of Sensitive Quasi-Identifiers for l-Diversity and t-ClosenessIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2017.269847216:4(580-593)Online publication date: 16-Jul-2019
https://dl.acm.org/doi/10.1109/TDSC.2017.2698472
Alnemari ARaj RRomanowski CMishra S(2019)Protecting Personally Identifiable Information (PII) in Critical Infrastructure Data Using Differential Privacy2019 IEEE International Symposium on Technologies for Homeland Security (HST)10.1109/HST47167.2019.9032942(1-6)Online publication date: Nov-2019
https://doi.org/10.1109/HST47167.2019.9032942
Wong KTu NBui DOoi SKim M(2019)Privacy-Preserving Collaborative Data Anonymization with Sensitive Quasi-Identifiers2019 12th CMI Conference on Cybersecurity and Privacy (CMI)10.1109/CMI48017.2019.8962140(1-6)Online publication date: Nov-2019
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Liu JXiong LLuo J(2018)Semantic SecurityTransactions on Data Privacy10.5555/2612272.26122736:3(185-198)Online publication date: 13-Dec-2018
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Ali-Eldin AZuiderwijk AJanssen M(2018)A Privacy Risk Assessment Model for Open DataBusiness Modeling and Software Design10.1007/978-3-319-78428-1_10(186-201)Online publication date: 20-Mar-2018
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