skip to main content
10.1145/1183614.1183757acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
Article

k nearest neighbor classification across multiple private databases

Published: 06 November 2006 Publication History

Abstract

Distributed privacy preserving data mining tools are critical for mining multiple databases with a minimum information disclosure. We present a framework including a general model as well as multi-round algorithms for mining horizontally partitioned databases using a privacy preserving k Nearest Neighbor (kNN) classifier.

References

[1]
G. Aggarwal, N. Mishra, and B. Pinkas. Secure computation of the kth ranked element. In IACR Conference on Eurocrypt, 2004.
[2]
S. Chitti, L. Xiong, and L. Liu. Mining multiple private databases using a privacy preserving knn classifier. Technical report, Emory University, Department of Mathematics and Computer Science, 2006. TR-2006-008-A.
[3]
C. Clifton, M. Kantarcioglu, X. Lin, J. Vaidya, and M. Zhu. Tools for privacy preserving distributed data mining. In SIGKDD Explorations, 2003.
[4]
O. Goldreich. Secure multi-party computation, 2001. Working Draft, Version 1.3.
[5]
V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin, and Y. Theodoridis. State-of-the-art in privacy preserving data mining. ACM SIGMOD Record, 33(1), 2004.
[6]
L. Xiong, S. Chitti, and L. Liu. Topk queries across multiple private databases. In 25th International Conference on Distributed Computing Systems (ICDCS), 2005.

Cited By

View all
  • (2022)A novel cryptographic protocol for privacy preserving classification over distributed encrypted databasesJournal of Banking and Financial Technology10.1007/s42786-022-00042-z6:1(31-41)Online publication date: 25-May-2022
  • (2021) Efficient homomorphic evaluation of k -NN classifiers Proceedings on Privacy Enhancing Technologies10.2478/popets-2021-00202021:2(111-129)Online publication date: 29-Jan-2021
  • (2020)Privacy-Preserving K-Nearest Neighbors Training over Blockchain-Based Encrypted Health DataElectronics10.3390/electronics91220969:12(2096)Online publication date: 9-Dec-2020
  • Show More Cited By

Index Terms

  1. k nearest neighbor classification across multiple private databases

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management
    November 2006
    916 pages
    ISBN:1595934332
    DOI:10.1145/1183614
    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: 06 November 2006

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. k nearest neighbor
    2. classification
    3. distributed databases
    4. privacy

    Qualifiers

    • Article

    Conference

    CIKM06
    CIKM06: Conference on Information and Knowledge Management
    November 6 - 11, 2006
    Virginia, Arlington, USA

    Acceptance Rates

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

    Upcoming Conference

    CIKM '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 19 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)A novel cryptographic protocol for privacy preserving classification over distributed encrypted databasesJournal of Banking and Financial Technology10.1007/s42786-022-00042-z6:1(31-41)Online publication date: 25-May-2022
    • (2021) Efficient homomorphic evaluation of k -NN classifiers Proceedings on Privacy Enhancing Technologies10.2478/popets-2021-00202021:2(111-129)Online publication date: 29-Jan-2021
    • (2020)Privacy-Preserving K-Nearest Neighbors Training over Blockchain-Based Encrypted Health DataElectronics10.3390/electronics91220969:12(2096)Online publication date: 9-Dec-2020
    • (2020) Secure and Efficient k NN Classification for Industrial Internet of Things IEEE Internet of Things Journal10.1109/JIOT.2020.29923497:11(10945-10954)Online publication date: Nov-2020
    • (2020)K-NN SEMANTIC Inquiry on Scrambled Social Information BASEICDSMLA 201910.1007/978-981-15-1420-3_108(982-991)Online publication date: 19-May-2020
    • (2018)Efficient and Secure kNN Classification over Encrypted Data Using Vector Homomorphic Encryption2018 IEEE International Conference on Communications (ICC)10.1109/ICC.2018.8422438(1-7)Online publication date: May-2018
    • (2018)Privacy Preserving Data Mining: A Review of the State of the ArtHarmony Search and Nature Inspired Optimization Algorithms10.1007/978-981-13-0761-4_1(1-15)Online publication date: 24-Aug-2018
    • (2017)Differentially private nearest neighbor classificationData Mining and Knowledge Discovery10.1007/s10618-017-0532-z31:5(1544-1575)Online publication date: 1-Sep-2017
    • (2016)A Multi-Objective Flow Cytometry Profiling for B-Cell Lymphoma DiagnosisProceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/2975167.2975169(23-31)Online publication date: 2-Oct-2016
    • (2016)Privacy-Preserving k-Nearest Neighbor Computation in Multiple Cloud EnvironmentsIEEE Access10.1109/ACCESS.2016.26335444(9589-9603)Online publication date: 2016
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media