ANONYMIZING CLASSIFICATION DATA FOR PRIVACY PRESERVATION PDF

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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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Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. Training a classifier requires accessing a large collection of data. Citations Publications citing this paper. Yu 21st International Conference on Data Engineering…. Showing of 3 references.

Skip to search form Skip to main content. Showing of extracted citations. AB – Classification is a fundamental problem in data analysis. Fung and Ke Wang and Philip S.

Anonymizing Classification Data for Privacy Preservation

A useful approach to combat such linking attacks, called k-anonymization [1], is anonymizing the linking attributes so that at least k released records prreservation each value combination of the linking attributes. This paper has highly influenced 20 other papers. Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ Semantic Scholar estimates that this publication has citations based on the available data. Data anonymization Privacy Distortion.

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Anonymizing classification data for privacy preservation

FungKe WangPhilip S. Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements.

Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. Topics Discussed in This Paper. Classification is a fundamental problem in data analysis.

In this paper, we propose a k-anonymization solution for classification. References Publications referenced by this paper. See our FAQ for additional information. Anonymizing Classification Data for Privacy Preservation. Link to publication in Scopus. By clicking privxcy or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.

Abstract Classification is a fundamental problem in data analysis. Link to citation list in Scopus. Classification is a fundamental problem in data analysis.

Anonymizing classification data for privacy preservation — UICollaboratory Research Profiles

Enhanced anonymization algorithm to preserve confidentiality of data in public cloud Amalraj IrudayasamyArockiam Lawrence International Conference on Information Society…. Real life Statistical classification Requirement. Our goal is to find a k-anonymization, not necessarily optimal daha the sense of minimizing date distortion, which preserves the classification structure. Anonymizing classification data for privacy preservation.

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We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data. 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. Top-down specialization for information and privacy preservation Benjamin C.

From This Paper Topics from this paper. This paper has citations. Training a classifier requires accessing a large collection of data.

Transforming data to satisfy privacy constraints Vijay S. Access to Document N2 – Classification is a fundamental problem in data analysis.

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