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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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.

Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements. Top-down specialization for information and privacy preservation Benjamin C.

Anonymizing classification data for privacy preservation. Datw goal is to find a k-anonymization, not necessarily optimal in the sense of minimizing date distortion, which preserves the classification structure. Enhanced anonymization algorithm to preserve confidentiality of data in public cloud Amalraj IrudayasamyArockiam Lawrence International Conference on Information Society….

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Anonymizing classification data for privacy preservation

Real life Statistical classification Requirement. 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.

Data anonymization Privacy Priivacy.

Showing of 3 references. Access to Document See our FAQ for additional information. Transforming data to satisfy privacy constraints Vijay S. Abstract Classification is a fundamental problem in data analysis. Showing of extracted citations. We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data.

Anonymizing Classification Data for Privacy Preservation. Training a classifier requires accessing a large collection of data. Topics Discussed in This Paper. Classification is a fundamental problem in data analysis. References Publications referenced by this paper. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.

Semantic Scholar estimates that this publication has citations based on the available data. Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric.

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Anonymizing Classification Data for Privacy Preservation – Semantic Scholar

FungKe WangZnonymizing S. Yu 21st International Conference on Data Engineering…. From This Paper Topics from this paper. Classification is a fundamental problem in data analysis. Citations Publications citing this paper. Skip to search form Skip to main content. In this paper, we propose a k-anonymization solution for classification.

Fung and Ke Wang and Philip S. This paper has highly influenced 20 other papers.

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Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. Link to citation list in Scopus. AB – Classification is a fundamental problem in data analysis. This paper has citations. Training a classifier requires accessing a large collection of data.

N2 preservatjon Classification is a fundamental problem in data analysis.