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An R-Based Landscape Validation of a Competing Risk Model
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Digression and Value Concatenation to Enable Privacy-Preserving Regression.

Xiao-Bai Li1, Sumit Sarkar2

  • 1Department of Operations and Information Systems, Manning School of Business, University of Massachusetts Lowell, Lowell, MA 01854 U.S.A. { xiaobai_li@uml.edu }

MIS Quarterly : Management Information Systems
|January 12, 2016
PubMed
Summary
This summary is machine-generated.

Regression attacks can expose sensitive data using regression trees. This study introduces "digression" to assess risk and prune trees, protecting privacy while preserving data utility.

Keywords:
Privacyanonymizationdata analyticsdata miningregressionregression trees

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Area of Science:

  • Computer Science
  • Data Privacy
  • Machine Learning

Background:

  • Regression techniques are susceptible to inferring private individual data.
  • Existing privacy-preserving methods are inadequate against regression attacks.

Purpose of the Study:

  • To address the novel problem of regression attacks on sensitive data.
  • To propose a new privacy-preserving approach against such attacks.

Main Methods:

  • Introduced a novel measure, 'digression', to assess sensitive data disclosure risk.
  • Developed a tree-pruning algorithm using digression to limit data disclosure.
  • Proposed a dynamic value-concatenation method for data anonymization.

Main Results:

  • The proposed approach effectively protects sensitive data privacy.
  • The method preserves data utility for research and analysis.
  • Demonstrated effectiveness on real-world financial, economic, and healthcare data.

Conclusions:

  • The digression measure and associated algorithms offer a robust defense against regression attacks.
  • The dynamic value-concatenation method enhances data anonymization efficacy.
  • The approach is versatile for both numeric and categorical data anonymization.