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
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
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(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
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
An efficient hash-based algorithm for minimal k-anonymity
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
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
ICDT'05 Proceedings of the 10th international conference on Database Theory
A family of enhanced (L,α)-diversity models for privacy preserving data publishing
Future Generation Computer Systems
Extended k-anonymity models against sensitive attribute disclosure
Computer Communications
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Data anonymization techniques based on enhanced privacy principles have been the focus of intense research in the last few years. All existing methods achieving privacy principles assume implicitly that the data objects to be anonymized are given once and fixed, which makes it unsuitable for time evolving data. However, in many applications, the real world data sources are dynamic. In such dynamic environments, the current techniques may suffer from poor data quality and/or vulnerability to inference. In this paper, we investigate the problem of updating large time-evolving microdata based on the sophisticated l -diversity model, in which it requires that every group of indistinguishable records contains at least l distinct sensitive attribute values; thereby the risk of attribute disclosure is kept under 1/l . We analyze how to maintain the l -diversity against time evolving updating. The experimental results show that the updating technique is very efficient in terms of effectiveness and data quality.