Protecting Respondents' Identities in Microdata Release
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
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
Checking for k-anonymity violation by views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
ANGEL: Enhancing the Utility of Generalization for Privacy Preserving Publication
IEEE Transactions on Knowledge and Data Engineering
The hardness and approximation algorithms for l-diversity
Proceedings of the 13th International Conference on Extending Database Technology
Anonymous Publication of Sensitive Transactional Data
IEEE Transactions on Knowledge and Data Engineering
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Although many algorithms have been developed to achieve privacy preserving data publishing, few of them can handle incomplete microdata. In this paper, we first show that traditional algorithms based on suppression and generalization cause huge information loss on incomplete microdata. Then, we propose AIM (anatomy for incomplete microdata), a linear-time algorithm based on anatomy, aiming to retain more information in incomplete microdata. Different from previous algorithms, AIM treats missing values as normal value, which greatly reduce the number of records being suppressed. Compared to anatomy, AIM supports more kinds of datasets, by employing a new residue-assignment mechanism, and is applicable to all privacy principles. Results of extensive experiments based on real datasets show that AIM provides highly accurate aggregate information for the incomplete microdata.