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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Abstract Classification is a fundamental problem in data analysis. Transforming data to satisfy privacy constraints Vijay S. 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

By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Service preservatioon, and Dataset License. Link to citation list in Scopus. AB – Classification is a fundamental problem in data analysis. Classification is a fundamental problem in data analysis. From This Paper Topics from this paper. Data anonymization Privacy Distortion.

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Yu 21st International Conference on Data Engineering…. 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. This paper has highly influenced 20 other papers. Clasification of extracted citations. 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.

Top-down specialization for information and privacy preservation Benjamin C. Citations Publications citing this paper. Access to Document Training a classifier requires accessing a large collection of data.

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…. Link to publication in Scopus.

Anonymizing classification data for privacy preservation

Classification is a fundamental problem in data analysis. 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. Fung and Ke Wang and Philip S. N2 – Classification is a fundamental problem in data analysis.

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Anonymizing Classification Data for Privacy Preservation

Topics Discussed in 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. In this paper, we propose anony,izing k-anonymization solution for classification. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric.

Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual’s privacy. Skip to search form Skip to main content.

Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements. FungKe WangPhilip S. Semantic Scholar estimates that this publication has citations based on the available data. See our FAQ for additional information. Showing of 3 references. Training a classifier requires accessing a large collection of data. This paper has citations. Real life Statistical classification Requirement.

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