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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Training a classifier requires accessing a large collection of data.

Anonymizing classification data for privacy preservation

This paper has highly influenced 20 other papers. Topics Discussed in This Paper. Link to citation list in Scopus.

Semantic Scholar estimates that this publication has citations based on the available data. Real life Statistical classification Requirement. FungKe WangPhilip S. Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements. Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ Showing of 3 references. Training a classifier requires accessing a large collection of data. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric.

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Enhanced anonymization algorithm to preserve confidentiality of data in public cloud Amalraj IrudayasamyArockiam Lawrence International Conference on Information Society…. Data anonymization Privacy Distortion. 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.

Citations Publications citing this paper.

N2 – Classification is a fundamental problem in data analysis. From This Paper Topics from this paper. Our goal is to find a k-anonymization, not necessarily optimal in the sense of minimizing date distortion, which preserves the classification structure.

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Yu 21st International Conference on Data Engineering…. In this paper, we propose a k-anonymization solution for classification. This paper has citations. Skip to search form Skip to main content. Access to Document References Publications referenced by this paper. AB – Classification is a fundamental problem in data analysis. Transforming data to satisfy privacy constraints Vijay S.

Anonymizing classification data for privacy preservation.

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Anonymizing Classification Data for Privacy Preservation. 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. 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

Abstract Classification is a fundamental problem in data analysis. Fung and Ke Wang and Philip S. Classification is a fundamental problem in data analysis. See our FAQ for additional information.

Anonymizing Classification Data for Privacy Preservation

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

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 wnonymizing Benjamin C.

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