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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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Semantic Scholar estimates that this publication has citations based on the available data. 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.

Link to citation list in Scopus.

Anonymizing classification data for privacy preservation

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

Anonymizing classification data for privacy preservation. Topics Discussed in This Paper. References Publications referenced by this paper.

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Anonymizing Classification Data for Privacy Preservation – Semantic Scholar

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.

Link to publication in Scopus. Transforming data to satisfy privacy constraints Fata S. Enhanced anonymization algorithm to preserve confidentiality of data in public cloud Amalraj IrudayasamyArockiam Lawrence International Conference on Information Society…. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. Experiments on real-life data show that the classificatiln of classification can be presefvation even for highly restrictive anonymity requirements.

Showing of extracted citations. AB – Classification is a fundamental problem in data analysis. This paper has citations. Anonymizing Classification Data for Privacy Preservation. Yu 21st International Conference on Data Engineering….

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. We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data. classifocation

Citations Publications citing this paper. Skip to search form Skip to main content. Real life Statistical classification Requirement. Our goal is to find a k-anonymization, not necessarily optimal in the sense of minimizing date distortion, which preserves the preservatino structure.

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Showing of 3 references. In this paper, we propose a k-anonymization solution for classification. Data anonymization Privacy Distortion. This paper has highly influenced 20 other papers. Citation Statistics Citations 0 20 40 ’09 ’12 ’15 ‘ Classification is a fundamental problem in data analysis.

Training a classifier requires accessing a large collection of data. Abstract Classification is a fundamental problem in data analysis. Top-down specialization for information and privacy preservation Benjamin C. Access to Document See our FAQ for additional information. Training a classifier requires accessing a large collection of data.

FungKe WangPhilip S. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. Classification is a fundamental problem in data analysis. From This Paper Topics from this paper.