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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Data anonymization Privacy Distortion.

Anonymizing Classification Data for Privacy Preservation

Classification is a fundamental problem in data analysis. Showing of extracted citations.

Training a classifier requires accessing a large collection of data. This paper has citations.

Fung and Ke Wang and Philip S. Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements. See our FAQ for additional information. References Publications referenced by this paper.

Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. From This Paper Topics from this paper. Anonymizing classification data for privacy preservation. 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 match each value combination of the linking attributes.

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Training a classifier requires accessing a large collection of data. Access to Document N2 – Classification is a fundamental problem in data analysis.

We argue that classificatiom the distortion to the training data is not relevant to the classification goal that requires extracting the structure of predication on the “future” data. Citations Publications citing this paper. Real life Statistical classification Requirement.

FungKe WangPhilip S. Link pgeservation citation list in Scopus. We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data.

Anonymizing classification data for privacy preservation — UICollaboratory Research Profiles

Showing of 3 references. Skip to search form Skip to main content. Top-down specialization for information and privacy preservation Benjamin C. Classification is a fundamental problem in data analysis. 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. Transforming data to satisfy privacy constraints Vijay S. Yu 21st International Conference on Data Engineering…. Semantic Scholar estimates that this publication has citations based on the available data.

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AB – Classfication is a fundamental problem in data analysis. Link to publication in Scopus. In this paper, we propose a k-anonymization solution for classification. Our goal is to find a k-anonymization, not necessarily optimal in the sense of minimizing date distortion, which preserves the classification structure.

Releasing person-specific data, such as customer prrivacy or patient records, may pose a threat to an individual’s privacy. Abstract Classification is a fundamental problem in data analysis. By clicking accept or continuing to use the site, preservstion agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.

Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ 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.

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