Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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
An efficient hash-based algorithm for minimal k-anonymity
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
ICDT'05 Proceedings of the 10th international conference on Database Theory
L-Diversity Based Dynamic Update for Large Time-Evolving Microdata
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Enhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing
Transactions on Data Privacy
Microdata protection through approximate microaggregation
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
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
An approximate microaggregation approach for microdata protection
Expert Systems with Applications: An International Journal
Priority driven k-anonymisation for privacy protection
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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One of the emerging concepts in microdata protection is k- anonymity, introduced by Samarati and Sweeney. k-anonymity provides a simple and efficient approach to protect private individual information and is gaining increasing popularity. k-anonymity requires that every tuple (record) in the microdata table released be indistinguishably related to no fewer than k respondents. In this paper, we introduce two new variants of the k-anonymity problem, namely, the Restricted k-anonymity problem and Restricted k-anonymity problem on attribute (where suppressing the entire attribute is allowed). We prove that both problems are NP-hard for k ≥ 3. The results imply the main results obtained by Meyerson and Williams. On the positive side, we develop a polynomial time algorithm for the Restricted 2-anonymity problem by giving a graphical representation of the microdata table.