Data distortion for privacy protection in a terrorist analysis system

Shuting Xu; Jun Zhang; Dianwei Han; Jie Wang

doi:10.1007/11427995_43

Data distortion for privacy protection in a terrorist analysis system

Shuting Xu, Jun Zhang, Dianwei Han, Jie Wang

Computer Science

Research output: Contribution to journal › Conference article › peer-review

28 Scopus citations

Abstract

Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets.

Original language	English
Pages (from-to)	459-464
Number of pages	6
Journal	Lecture Notes in Computer Science
Volume	3495
DOIs	https://doi.org/10.1007/11427995_43
State	Published - 2005
Event	IEEE International Conference on Intelligence and Security Informatics, ISI 2005 - Atlanta, GA, United States Duration: May 19 2005 → May 20 2005

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/11427995_43

Cite this

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abstract = "Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets.",

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AU - Zhang, Jun

AU - Han, Dianwei

AU - Wang, Jie

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AB - Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets.

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U2 - 10.1007/11427995_43

DO - 10.1007/11427995_43

M3 - Conference article

AN - SCOPUS:24944561624

SN - 0302-9743

VL - 3495

SP - 459

EP - 464

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - IEEE International Conference on Intelligence and Security Informatics, ISI 2005

Y2 - 19 May 2005 through 20 May 2005

ER -

Data distortion for privacy protection in a terrorist analysis system

Abstract

ASJC Scopus subject areas

UN SDGs

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