Practical data-swapping: the first steps
ACM Transactions on Database Systems (TODS)
C4.5: programs for machine learning
C4.5: programs for machine learning
Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Privacy Preserving Data Mining (Advances in Information Security)
Privacy Preserving Data Mining (Advances in Information Security)
A new scheme on privacy-preserving data classification
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
International Journal of Business Information Systems
Developing privacy solutions for sharing and analysing healthcare data
International Journal of Business Information Systems
International Journal of Business Information Systems
Investigating the relationship among self-leadership strategies by association rules mining
International Journal of Business Information Systems
Evaluating and ranking hotels offering e-service by integrated approach of Webqual and fuzzy AHP
International Journal of Business Information Systems
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Sharing of data among multiple organisations is required in many situations. The shared data may contain sensitive information about individuals which if shared may lead to privacy breach. Thus, maintaining the individual privacy is a great challenge. In order to overcome the challenges involved in data mining, when data needs to be shared, privacy preserving data mining (PPDM) has evolved as a solution. The objective of PPDM is to have the interesting knowledge mined from the data at the same time to maintain the individual privacy. This paper addresses the problem of PPDM by transforming the attributes to fuzzy attributes. Thus, the individual privacy is also maintained, as one cannot predict the exact value, at the same time, better accuracy of mining results is achieved. ID3 and Naive Bayes classification algorithms over three different datasets are used in the experiments to show the effectiveness of the approach.