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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Access to Document Yu 21st International Conference on Data Engineering…. References Publications referenced by this paper. See our FAQ for additional information.

Classification is a fundamental problem in data analysis. We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data.

Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. In this paper, we propose a k-anonymization solution for classification. Real life Statistical classification Requirement. Training a classifier requires accessing a large collection of data.

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AB – Classification is a fundamental problem in data analysis. From This Paper Topics from this paper.

This paper has highly influenced 20 other papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. Top-down specialization for information and privacy preservation Benjamin C.

N2 – Classification is a fundamental problem in data annonymizing.

Anonymizing classification data for privacy preservation — UICollaboratory Research Profiles

Data anonymization Privacy Distortion. Link to publication in Scopus. FungKe WangPhilip S. Transforming data to satisfy privacy constraints Vijay S. Showing of 3 references. This paper has citations. Abstract Classification is a fundamental problem in data analysis. Skip to search form Skip to main content.

Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. Link to citation list in Scopus.

Anonymizing Classification Data for Privacy Preservation

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Classification is a fundamental problem in data analysis. Experiments on real-life rata show that the quality of classification can be preserved even for highly restrictive anonymity requirements.

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A useful approach to combat such linking attacks, called k-anonymization [1], is anonymizing the linking attributes so that at least k released records match each value combination of the linking attributes. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. We argue that minimizing the distortion to anonymmizing training data is not relevant to the classification goal that requires extracting the structure of predication on the “future” data.

Anonymizing Classification Data for Privacy Preservation – Semantic Scholar

Training a classifier requires accessing a large collection of data. Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ Fung and Ke Wang and Philip S. Our goal is to find a k-anonymization, not necessarily optimal in the sense of minimizing date distortion, which preserves the classification structure.