Article

Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data

Authors:

Xiaoqian Jiang,

Jaideep VaidyaAuthors Info & Claims

SAC '06: Proceedings of the 2006 ACM symposium on Applied computing

Pages 603 - 610

https://doi.org/10.1145/1141277.1141415

Published: 23 April 2006 Publication History

Abstract

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What we need is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the non-disclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from the data distributed at multiple parties, without disclosing the data of each party to others. We assume that data is horizontally partitioned -- each party collects the same features of information for different data objects. We quantify the security and efficiency of the proposed method, and highlight future challenges.

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Cited By

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Abdelfattah SBaza MMahmoud MFouda MAbualsaud KYaacoub EAlsabaan MGuizani M(2023)Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy PreservationSensors10.3390/s2322903323:22(9033)Online publication date: 8-Nov-2023
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Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data

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cover image ACM Conferences

SAC '06: Proceedings of the 2006 ACM symposium on Applied computing

April 2006

1967 pages

ISBN:1595931082

DOI:10.1145/1141277

Conference Chair:
Hisham M. Haddad
Kennesaw State University, Kennesaw, Georgia

Copyright © 2006 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 April 2006

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April 23 - 27, 2006

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Chaudhary RKumar RSaxena N(2024)A systematic review on federated learning system: a new paradigm to machine learningKnowledge and Information Systems10.1007/s10115-024-02257-667:2(1811-1914)Online publication date: 9-Nov-2024
https://doi.org/10.1007/s10115-024-02257-6
Abdelfattah SBaza MMahmoud MFouda MAbualsaud KYaacoub EAlsabaan MGuizani M(2023)Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy PreservationSensors10.3390/s2322903323:22(9033)Online publication date: 8-Nov-2023
https://doi.org/10.3390/s23229033
BORISOV ABOSOV AIVANOV A(2023)APPLICATION OF COMPUTER SIMULATION TO THE ANONYMIZATION OF PERSONAL DATA: STATE-OF-THE-ART AND KEY POINTSПрограммирование10.31857/S0132347423040040(58-74)Online publication date: 1-Jul-2023
https://doi.org/10.31857/S0132347423040040
Borisov ABosov AIvanov A(2023)Application of Computer Simulation to the Anonymization of Personal Data: State-of-the-Art and Key PointsProgramming and Computer Software10.1134/S036176882304004749:4(232-246)Online publication date: 28-Jul-2023
https://doi.org/10.1134/S0361768823040047
Guo GZhu YChen EYu RZhang LLv KFeng R(2023)Efficient Multiparty Fully Homomorphic Encryption With Computation Fairness and Error Detection in Privacy Preserving Multisource Data MiningIEEE Transactions on Reliability10.1109/TR.2023.324656372:4(1308-1323)Online publication date: Dec-2023
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Hannemann AÜnal ASwaminathan ABuchmann EAkgün M(2023)A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)10.1109/TPS-ISA58951.2023.00020(82-90)Online publication date: 1-Nov-2023
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Li QWen ZWu ZHu SWang NLi YLiu XHe B(2023)A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312459935:4(3347-3366)Online publication date: 1-Apr-2023
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Xie YZhao JZhu BLi JLi RXue K(2023)Achieving Privacy-Preserving Outsourced SVM Training with Non-Linear KernelGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10437212(7115-7120)Online publication date: 4-Dec-2023
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Li MLin YChen HAparasu R(2023)An unsupervised embedding harmonization system for privacy-preserving data mining in healthcareIISE Transactions on Healthcare Systems Engineering10.1080/24725579.2023.223101114:1(1-17)Online publication date: 25-Jul-2023
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Navia-Vázquez ADíaz-Morales RFernández-Díaz M(2022)Budget Distributed Support Vector Machine for Non-ID Federated Learning ScenariosACM Transactions on Intelligent Systems and Technology10.1145/353973413:6(1-25)Online publication date: 22-Sep-2022
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