A General Additive Data Perturbation Method for Database Security

Muralidhar, Krishnamurty; Parsa, Rahul; Sarathy, Rathindra

doi:10.1287/mnsc.45.10.1399

Cited by 144 publications

(112 citation statements)

References 7 publications

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“…They developed two transportation flow algorithms that are computationally efficient and insightful. Muralidhar et al (1999) developed a model to explain how to use data-perturbation methods to protect information from unwanted access while allowing maximum access to genuine inquiries and maintaining the relationships between attributes.…”

Section: Cyber Securitymentioning

confidence: 99%

A Survey of Operations Research Models and Applications in Homeland Security

2006

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Operations research has had a long and distinguished history of work in emergency preparedness and response, airline security, transportation of hazardous materials, and threat and vulnerability analysis. Since the attacks of September 11, 2001 and the formation of the US Department of Homeland Security, these topics have been gathered under the broad umbrella of homeland security. In addition, other areas of OR applications in homeland security are evolving, such as border and port security, cyber security, and critical infrastructure protection. The opportunities for operations researchers to contribute to homeland security remain numerous.

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Section: Cyber Securitymentioning

confidence: 99%

A Survey of Operations Research Models and Applications in Homeland Security

2006

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“…Quantifying privacy: Traditionally, the privacy provided by a perturbation technique has been measured as the variance between the actual and the perturbed values [12] . This measure is given by Var(X-Y) where X represents a single original attribute and Y the distorted attribute.…”

Section: Resultsmentioning

confidence: 99%

An Effective Data Transformation Approach for Privacy Preserving Clustering

Rajalaxmi¹,

Natarajan²

2008

J. of Computer Science

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Abstract:A new stream of research privacy preserving data mining emerged due to the recent advances in data mining, Internet and security technologies. Data sharing among organizations considered to be useful which offer mutual benefit for business growth. Preserving the privacy of shared data for clustering was considered as the most challenging problem. To overcome the problem, the data owner published the data by random modification of the original data in certain way to disguise the sensitive information while preserving the particular data property. Data transformation techniques played a vital role to preserve privacy in data mining. We put forward an effective approach which defeats the problem of addressing privacy of confidential categorical data in clustering. A set of hybrid data transformations are introduced (HDTTR and HDTSR) and the effectiveness of the approach has been analyzed. A complete analysis of the proposed approach and a formal study of the problem have been done. Our proposed approach illustrates the effectiveness of clustering of sensitive categorical data before and after the transformation.

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“…In other cases, noise is added to confidential attributes by randomly shuffling the attribute values to prevent the discovery of some patterns that are not supposed to be discovered. We categorize noise addition techniques into three groups: (1) data swapping techniques; (2) data perturbation techniques; and (3) data randomization techniques [16]. …”

Section: Noise Addition Techniquementioning

confidence: 99%

Hybrid Perturbation Technique using Feature Selection Method for Privacy Preservation in Data Mining

Priyadarsini¹,

Valarmathi²,

Sivakumari³

2012

IJCA

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Privacy-preserving in data mining refers to the area of data mining that seeks to safeguard sensitive information from unsolicited or unsanctioned disclosure and hence protecting individual data records and their privacy. Data perturbation is a privacy preservation technique which does addition / multiplication of noise to the original data. It performs anonymization based on the data type of sensitive data. Generalization is a technique were quasi identifiers data are replaced by some other more general term. In this paper privacy protection is applied to high dimensional datasets like Adult and Census. For ranking the attributes, information gain feature subset selection method is used. The high ranking attributes with sensitive information are set as quasi identifiers of the datasets. A hybrid perturbation technique is used to perturb categorical and numeric attributes of both the datasets and the utility of the datasets is measured using accuracy on data mining functionalities. The data distortion is measured using maintenance of Rank of Features (CK) between the original and perturb datasets. Experimental results show that utility of the perturbed datasets comparable with the original dataset and the Census dataset has comparable CK value than adult dataset.

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A General Additive Data Perturbation Method for Database Security

Cited by 144 publications

References 7 publications

A Survey of Operations Research Models and Applications in Homeland Security

A Survey of Operations Research Models and Applications in Homeland Security

An Effective Data Transformation Approach for Privacy Preserving Clustering

Hybrid Perturbation Technique using Feature Selection Method for Privacy Preservation in Data Mining

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