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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In this paper, we propose a k-anonymization solution for classification.

Anonymizing Classification Data for Privacy Preservation

Enhanced anonymization algorithm to preserve confidentiality of data in public cloud Amalraj IrudayasamyArockiam Lawrence International Conference on Information Society…. Topics Discussed in This Paper. Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy.

Link to publication in Scopus. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric.

Semantic Scholar estimates that this publication has citations based on the available data. Top-down specialization for information and privacy preservation Benjamin C.

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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.

By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. This paper has citations. 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.

N2 – Classification is a fundamental problem in data analysis. 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. Showing of 3 references. Real life Statistical classification Requirement. AB – Classification is a fundamental problem in data analysis. Yu 21st International Conference on Data Engineering….

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Anonymizing Classification Data for Privacy Preservation – Semantic Scholar

Classification is a fundamental problem in data analysis. FungKe WangPhilip S. Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ This paper has highly influenced 20 other papers.

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

Anonymizing classification data for privacy preservation

We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data. Training a classifier requires accessing a large collection classificatioh data.

Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements. Training a classifier requires accessing a large collection of data. Data anonymization Privacy Distortion. Access to Document Skip to search form Skip to main content.

From This Paper Topics from this paper. Anonymizing classification data for privacy preservation. Fung and Ke Wang and Philip S. 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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