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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Showing of extracted citations. Anonymizing classification data for privacy preservation. Skip to search form Skip to main content. Classification is a fundamental problem in data analysis.

Top-down specialization for information and privacy preservation Benjamin C. See our FAQ for additional information.

Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. Semantic Scholar estimates that this publication has citations based on the available data.

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Anonymizing Classification Data for Privacy Preservation

This paper has highly influenced 20 other papers. Fung and Ke Wang and Philip S. Anonymizing Classification Data for Privacy Preservation.

Presedvation data to satisfy privacy constraints Vijay S. Yu 21st International Conference on Data Engineering…. Training a classifier requires accessing a large collection of 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. This paper has citations. Showing of 3 references.

From This Paper Topics from this paper. Link to citation list in Scopus.

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In this paper, we propose a k-anonymization solution for classification. Enhanced anonymization algorithm to preserve confidentiality of data in public cloud Amalraj IrudayasamyArockiam Lawrence International Conference on Information Society….

Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ Our goal is to find a k-anonymization, not necessarily optimal in the sense of minimizing date distortion, which preserves the classification structure. Data anonymization Privacy Distortion. FungKe WangPhilip Preservaion. Classification is a fundamental problem in data analysis.

Anonymizing classification data for privacy preservation — UICollaboratory Research Profiles

Abstract Classification is a fundamental problem in prlvacy analysis. 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.

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We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data. Link to publication in Scopus. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. References Publications referenced by this paper. AB – Classification is a fundamental problem in data analysis.

N2 – Classification is a fundamental problem in data analysis. Real life Statistical classification Requirement. Citations Publications citing this paper. Access to Preservatipn Training a classifier requires accessing a large collection of data. Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements. Topics Discussed in This Paper.