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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Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements. This paper has highly influenced 20 other papers. Semantic Scholar estimates that this publication has citations based on the available data. AB – Classification is a fundamental problem in data analysis.

By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. Yu 21st International Conference on Data Engineering…. Our goal is to find a k-anonymization, not necessarily optimal in the sense of minimizing date distortion, which preserves the classification structure. Access to Document Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy.

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Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. This paper has citations. References Publications referenced by this paper. Link to publication in Scopus. 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.

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…. Citation Statistics Citations pprivacy 20 40 ’09 ’12 ’15 ‘ We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data. Topics Discussed in This Paper.

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

In this paper, we propose a k-anonymization solution for classification. Real life Statistical classification Requirement. Abstract Classification is a fundamental problem in data analysis.

Showing of 3 references. Transforming data to satisfy privacy constraints Vijay S. Anonymizing classification data for privacy preservation.

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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.

Training a classifier requires aonymizing a large collection of data. Fung and Ke Wang and Philip S. Citations Publications citing this paper. See our FAQ for additional information.

Anonymizing classification data for privacy preservation

Showing of extracted citations. Classification is a fundamental problem in data analysis. Link to citation list in Scopus. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. Anonymizing Classification Data for Privacy Preservation. Skip to search form Skip to main content.

Anonymizing Classification Data for Privacy Preservation

N2 – Classification is a fundamental problem in data analysis. From This Paper Topics from this paper.

Data anonymization Privacy Distortion. FungKe WangPhilip S.