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Privacy-preserving mining by rotational data transformation

Published: 18 March 2005 Publication History

Abstract

Many data mining applications deal with large data sets that contain private information that must be protected. This has led to the development of many privacy-preserving data mining techniques. Many of these techniques use randomized data distortion by adding noise to the sensitive data. However, non-careful noise addition may introduce biases to the statistical parameters of these data, including means and variances. To meet privacy requirements and preserve the statistical properties of the sensitive data we use a data transformation technique called Rotation-Based Transformation (RBT). This method distorts only confidential numerical attributes and preserves the statistical properties of the data.

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Cited By

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  • (2023)Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-023-10425-356:9(10427-10464)Online publication date: 22-Feb-2023
  • (2021)Recent Developments in Privacy-preserving Mining of Clinical DataACM/IMS Transactions on Data Science10.1145/34477742:4(1-32)Online publication date: 15-Nov-2021
  • (2014)Privacy Preserving Data Mining Using General Regression Auto-Associative Neural Network: Application to Regression ProblemsSwarm, Evolutionary, and Memetic Computing10.1007/978-3-319-20294-5_53(618-624)Online publication date: 18-Dec-2014
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    cover image ACM Conferences
    ACMSE '05 vol 1: Proceedings of the 43rd annual ACM Southeast Conference - Volume 1
    March 2005
    408 pages
    ISBN:1595930590
    DOI:10.1145/1167350
    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: 18 March 2005

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    Author Tags

    1. data mining
    2. data transformation
    3. privacy

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    ACM SE05
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    ACM SE05: ACM Southeast Regional Conference 2005
    March 18 - 20, 2005
    Georgia, Kennesaw

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    Overall Acceptance Rate 502 of 1,023 submissions, 49%

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    View all
    • (2023)Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-023-10425-356:9(10427-10464)Online publication date: 22-Feb-2023
    • (2021)Recent Developments in Privacy-preserving Mining of Clinical DataACM/IMS Transactions on Data Science10.1145/34477742:4(1-32)Online publication date: 15-Nov-2021
    • (2014)Privacy Preserving Data Mining Using General Regression Auto-Associative Neural Network: Application to Regression ProblemsSwarm, Evolutionary, and Memetic Computing10.1007/978-3-319-20294-5_53(618-624)Online publication date: 18-Dec-2014
    • (2009)Privacy preservation in data mining using hybrid perturbation methods: an application to bankruptcy prediction in banksInternational Journal of Data Analysis Techniques and Strategies10.1504/IJDATS.2009.0275091:4(313-331)Online publication date: 1-Jul-2009
    • (2009)Privacy preserving churn predictionProceedings of the 2009 ACM symposium on Applied Computing10.1145/1529282.1529643(1610-1614)Online publication date: 8-Mar-2009
    • (2009)Privacy preservation in k-means clustering by cluster rotationTENCON 2009 - 2009 IEEE Region 10 Conference10.1109/TENCON.2009.5396140(1-7)Online publication date: Nov-2009

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