On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient hash-based algorithm for minimal k-anonymity
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
On the complexity of restricted k-anonymity problem
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Achieving k-anonymity by clustering in attribute hierarchical structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
ICDT'05 Proceedings of the 10th international conference on Database Theory
Systematic clustering method for l-diversity model
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
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Given the threat of re-identification in our growing digital society, guaranteeing privacy while providing worthwhile data for knowledge discovery has become a difficult problem. k-anonymity is a major technique used to ensure privacy by generalizing and suppressing attributes and has been the focus of intense research in the last few years. However, data modification techniques like generalization may produce anonymous data unusable for medical studies because some attributes become too coarse-grained. In this paper, we propose a priority driven k-anonymisation that allows to specify the degree of acceptable distortion for each attribute separately. We also define some appropriate metrics to measure the distance and information loss, which are suitable for both numerical and categorical attributes. Further, we formulate the priority driven k-anonymisation as the k-nearest neighbor (KNN) clustering problem by adding a constraint that each cluster contains at least k tuples. We develop an efficient algorithm for priority driven k-anonymisation. Experimental results show that the proposed technique causes significantly less distortions.