Article

Privacy preserving association rule mining in vertically partitioned data

Authors:

Jaideep Vaidya,

Chris CliftonAuthors Info & Claims

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 639 - 644

https://doi.org/10.1145/775047.775142

Published: 23 July 2002 Publication History

Abstract

Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association rules. However, the sites must not reveal individual transaction data. We present a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.

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Wang ZDraghi BRotalinti YLunn DMyles P(2024)High-Fidelity Synthetic Data Applications for Data AugmentationDeep Learning - Recent Findings and Researches [Working Title]10.5772/intechopen.113884Online publication date: 12-Jan-2024
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Shakeer SBabu M(2024)A Study of Federated Learning with Internet of Things for Data Privacy and Security using Privacy Preserving TechniquesRecent Patents on Engineering10.2174/187221211766623011211025718:1Online publication date: Jan-2024
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Privacy preserving association rule mining in vertically partitioned data

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Published In

cover image ACM Conferences

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

July 2002

719 pages

ISBN:158113567X

DOI:10.1145/775047

Conference Chair:
Osmar R. Zaïane
University of Alberta, Canada
,
General Chair:
Randy Goebel
University of Alberta, Canada
,
Program Chairs:
David Hand
Imperial College, UK
,
Daniel Keim
AT&T
,
Raymond Ng
University of British Columbia, Canada

Copyright © 2002 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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Published: 23 July 2002

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KDD02: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 23 - 26, 2002

Alberta, Edmonton, Canada

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KDD '02 Paper Acceptance Rate 44 of 307 submissions, 14%;

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

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Cited By

Wang ZDraghi BRotalinti YLunn DMyles P(2024)High-Fidelity Synthetic Data Applications for Data AugmentationDeep Learning - Recent Findings and Researches [Working Title]10.5772/intechopen.113884Online publication date: 12-Jan-2024
https://doi.org/10.5772/intechopen.113884
Shakeer SBabu M(2024)A Study of Federated Learning with Internet of Things for Data Privacy and Security using Privacy Preserving TechniquesRecent Patents on Engineering10.2174/187221211766623011211025718:1Online publication date: Jan-2024
https://doi.org/10.2174/1872212117666230112110257
Pessach DTassa TShmueli E(2024)Fairness-Driven Private Collaborative Machine LearningACM Transactions on Intelligent Systems and Technology10.1145/363936815:2(1-30)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3639368
Lyu LYu HMa XChen CSun LZhao JYang QYu P(2024)Privacy and Robustness in Federated Learning: Attacks and DefensesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321698135:7(8726-8746)Online publication date: Jul-2024
https://doi.org/10.1109/TNNLS.2022.3216981
Li SZeng YWang YZhong YZhou ZTong Y(2024)An Experimental Study on Federated Equi-JoinsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337502836:9(4443-4457)Online publication date: Sep-2024
https://doi.org/10.1109/TKDE.2024.3375028
Zhou QWu SJiang CZhang RJing X(2024)Cloud-Edge–Terminal Collaboration-Enabled Device-Free Sensing Under Class-Imbalance ConditionsIEEE Internet of Things Journal10.1109/JIOT.2023.331294111:4(5980-5992)Online publication date: 15-Feb-2024
https://doi.org/10.1109/JIOT.2023.3312941
Shankar ABrouwer HHai RChen L(2024)SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00016(110-123)Online publication date: 13-May-2024
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van Daalen FIppel LDekker ABermejo I(2024)VertiBayes: learning Bayesian network parameters from vertically partitioned data with missing valuesComplex & Intelligent Systems10.1007/s40747-024-01424-010:4(5317-5329)Online publication date: 25-Apr-2024
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Wu WHao J(2024)Privacy-preserving Apriori-based association rule mining over semantically secure encrypted cloud databasePeer-to-Peer Networking and Applications10.1007/s12083-024-01761-4Online publication date: 25-Sep-2024
https://doi.org/10.1007/s12083-024-01761-4
Khedr AAttiya IAl Ali A(2024)EDNBC: A New Efficient Distributed Naive Bayes Classifier for Vertically Distributed DataModern Artificial Intelligence and Data Science 202410.1007/978-3-031-65038-3_38(475-488)Online publication date: 4-Oct-2024
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