ANONYMIZING CLASSIFICATION DATA FOR PRIVACY PRESERVATION PDF

PDF | Classification of data with privacy preservation is a fundamental problem in privacy preserving data mining. The privacy goal requires. Classification is a fundamental problem in data analysis. Training a classifier requires accessing a large collection of data. Releasing. Classification of data with privacy preservation is a fundamental One way to achieve both is to anonymize the dataset that contains the.

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A useful approach to combat such linking attacks, called k-anonymization [1], is anonymizing the linking attributes so that gor least k released records match each value combination of the linking attributes. Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy.

Link to classififation in Scopus. Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ Yu 21st International Conference on Data Engineering…. Topics Discussed in This Paper. Anonymizing classification data for privacy preservation.

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Showing of extracted citations. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. Data anonymization Privacy Distortion. Link to citation list in Scopus.

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Abstract Classification is a fundamental problem in data analysis. N2 – Classification is a fundamental problem in data analysis. Citations Publications citing this paper. From This Paper Topics from this paper. Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s datq.

Classification is a fundamental problem in data analysis. Real life Statistical classification Requirement. Anonymizing Classification Data for Privacy Preservation. Semantic Scholar estimates that this publication has citations based on the available data. We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data.

Training a classifier requires accessing a large collection of data. Classification is a fundamental problem in data analysis. Showing of 3 references.

Enhanced anonymization algorithm to preserve confidentiality of data in public cloud Amalraj IrudayasamyArockiam Lawrence International Conference on Information Society….

This paper has highly influenced 20 other papers. Fung and Ke Wang and Philip S. Training a classifier requires accessing a large collection of data.

See our FAQ for additional information. Access to Document Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements.

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References Publications referenced by this paper. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. Top-down specialization for information and privacy preservation Benjamin C. This paper has citations.

Anonymizing Classification Data for Privacy Preservation

We argue that minimizing the distortion to the training data is not relevant to the classification goal that requires extracting the structure of predication on the “future” data.

FungKe WangPhilip S.

In this paper, we propose a k-anonymization solution for classification. Our goal is to find a k-anonymization, not necessarily optimal preserrvation the sense of minimizing date distortion, which preserves the classification structure.

AB – Classification is a fundamental problem in data analysis. Transforming data to satisfy privacy constraints Vijay S.