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
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on 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
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
(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
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 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
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On the tradeoff between privacy and utility in data publishing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Injecting purpose and trust into data anonymisation
Proceedings of the 18th ACM conference on Information and knowledge management
Generating microdata with p-sensitive k-anonymity property
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
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
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
ICDT'05 Proceedings of the 10th international conference on Database Theory
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
Multivariate microaggregation by iterative optimization
Applied Intelligence
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p-Sensitive k-anonymity model has been recently defined as a sophistication of k-anonymity. This new property requires that there be at least p distinct values for each sensitive attribute within the records sharing a set of quasi-identifier attributes. In this paper, we identify the situations when the p-sensitive k-anonymity property is not enough for the sensitive attributes protection. To overcome the shortcoming of the p-sensitive k-anonymity principle, we propose two new enhanced privacy requirements, namely p^+-sensitive k-anonymity and (p,@a)-sensitive k-anonymity properties. These two new introduced models target at different perspectives. Instead of focusing on the specific values of sensitive attributes, p^+-sensitive k-anonymity model concerns more about the categories that the values belong to. Although (p,@a)-sensitive k-anonymity model still put the point on the specific values, it includes an ordinal metric system to measure how much the specific sensitive attribute values contribute to each QI-group. We make a thorough theoretical analysis of hardness in computing the data set that satisfies either p^+-sensitive k-anonymity or (p,@a)-sensitive k-anonymity. We devise a set of algorithms using the idea of top-down specification, which is clearly illustrated in the paper. We implement our algorithms on two real-world data sets and show in the comprehensive experimental evaluations that the two new introduced models are superior to the previous method in terms of effectiveness and efficiency.