research-article

Anonymizing transaction databases for publication

Authors:

Ada Wai-Chee Fu,

Philip S. YuAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 767 - 775

https://doi.org/10.1145/1401890.1401982

Published: 24 August 2008 Publication History

Abstract

This paper considers the problem of publishing "transaction data" for research purposes. Each transaction is an arbitrary set of items chosen from a large universe. Detailed transaction data provides an electronic image of one's life. This has two implications. One, transaction data are excellent candidates for data mining research. Two, use of transaction data would raise serious concerns over individual privacy. Therefore, before transaction data is released for data mining, it must be made anonymous so that data subjects cannot be re-identified. The challenge is that transaction data has no structure and can be extremely high dimensional. Traditional anonymization methods lose too much information on such data. To date, there has been no satisfactory privacy notion and solution proposed for anonymizing transaction data. This paper proposes one way to address this issue.

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Cited By

Wang RLiang JWang SChang C(2024)A divide-and-conquer approach to privacy-preserving high-dimensional big data releaseJournal of Information Security and Applications10.1016/j.jisa.2024.10375683(103756)Online publication date: Jun-2024
https://doi.org/10.1016/j.jisa.2024.103756
Alshuhail ABhatia S(2023)An Improved Partitioning Method via Disassociation towards Environmental SustainabilitySustainability10.3390/su1509744715:9(7447)Online publication date: 30-Apr-2023
https://doi.org/10.3390/su15097447
Kim S(2023)A New Approach for Anonymizing Transaction Data with Set ValuesElectronics10.3390/electronics1214304712:14(3047)Online publication date: 12-Jul-2023
https://doi.org/10.3390/electronics12143047
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Index Terms

Anonymizing transaction databases for publication

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Published In

cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 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]

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Publication History

Published: 24 August 2008

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KDD08

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KDD08: The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2008

Nevada, Las Vegas, USA

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

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

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Citations

Cited By

Wang RLiang JWang SChang C(2024)A divide-and-conquer approach to privacy-preserving high-dimensional big data releaseJournal of Information Security and Applications10.1016/j.jisa.2024.10375683(103756)Online publication date: Jun-2024
https://doi.org/10.1016/j.jisa.2024.103756
Alshuhail ABhatia S(2023)An Improved Partitioning Method via Disassociation towards Environmental SustainabilitySustainability10.3390/su1509744715:9(7447)Online publication date: 30-Apr-2023
https://doi.org/10.3390/su15097447
Kim S(2023)A New Approach for Anonymizing Transaction Data with Set ValuesElectronics10.3390/electronics1214304712:14(3047)Online publication date: 12-Jul-2023
https://doi.org/10.3390/electronics12143047
Zirui MBin G(2023)A Privacy-Preserved and User Self-Governance Blockchain-Based Framework to Combat COVID-19 Depression in Social MediaIEEE Access10.1109/ACCESS.2023.326459811(35255-35280)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3264598
Liu XFeng XZhu Y(2022)Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local SuppressionSymmetry10.3390/sym1403047214:3(472)Online publication date: 25-Feb-2022
https://doi.org/10.3390/sym14030472
Monsan AAdepo JN’zi EGoore B(2022)Addition-Based Algorithm to Overcome Cover Problem During Anonymization of Transactional DataIntelligent Computing10.1007/978-3-031-10461-9_62(896-914)Online publication date: 7-Jul-2022
https://doi.org/10.1007/978-3-031-10461-9_62
Puri VKaur PSachdeva S(2022) ( k , m , t )‐anonymity: Enhanced privacy for transactional data Concurrency and Computation: Practice and Experience10.1002/cpe.702034:18Online publication date: 10-Apr-2022
https://doi.org/10.1002/cpe.7020
Ouyang JXiao YLiu SXiao ZLiao X(2021)Set-valued data collection with local differential privacy based on category hierarchyMathematical Biosciences and Engineering10.3934/mbe.202113918:3(2733-2763)Online publication date: 2021
https://doi.org/10.3934/mbe.2021139
Babu MSreedevi M(2021)A Comprehensive Study on Enhanced Clustering Technique of Association Rules over Transactional Datasets2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC52330.2021.9640681(1-5)Online publication date: 11-Nov-2021
https://doi.org/10.1109/I-SMAC52330.2021.9640681
Cunha MMendes RVilela J(2021)A survey of privacy-preserving mechanisms for heterogeneous data typesComputer Science Review10.1016/j.cosrev.2021.10040341:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100403
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