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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This paper has highly influenced 20 other papers. Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. 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.

Real life Statistical classification Requirement. Classification is a fundamental problem in data analysis.

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

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Anonymizing classification data for privacy preservation — UICollaboratory Research Profiles

By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. Skip to search form Skip to main content. Top-down specialization for information and privacy pruvacy Benjamin C.

Data anonymization Privacy Distortion. Citations Publications citing this paper.

Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. Link to citation list in Scopus.

Fung and Ke Wang and Philip S.

Anonymizing classification data for privacy preservation

This paper has citations. References Publications referenced by this paper.

Anonymizing classification data for privacy preservation. Showing of extracted citations. Topics Discussed in This Paper.

Anonymizing Classification Data for Privacy Preservation

Anonymizing Classification Data for Privacy Preservation. In this paper, we propose a k-anonymization solution for classification. 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. Yu 21st International Conference on Data Engineering….

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Link to publication in Scopus. Showing of 3 references. AB – Classification is a fundamental problem in data analysis.

Access to Document Abstract Classification is a fundamental problem in data analysis. Transforming data to satisfy privacy constraints Vijay S. Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ Enhanced anonymization algorithm to preserve confidentiality of data in public cloud Amalraj IrudayasamyArockiam Lawrence International Conference on Information Society….

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. N2 – Classification is a fundamental problem in data analysis.

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