Elements of information theory
Elements of information theory
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
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
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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Approximate algorithms for K-anonymity
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
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Dynamic anonymization: accurate statistical analysis with privacy preservation
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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
Privacy preserving serial data publishing by role composition
Proceedings of the VLDB Endowment
On the tradeoff between privacy and utility in data publishing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
In this paper, we study how to sanitize the publishing data with sensitive attribute to achieve t-closeness and δ-disclosure privacy under Incognito framework. t-closeness is a privacy measure proposed to account for skewness attack and similarity attack, which are limitations of l-diversity. Under the t-closeness model, the distance between the privacy attribute distribution and the global one should be under the threshold t. Whereas semantic privacy (δ-disclosure privacy) is used to measure the incremental information gain from the anonymized tables. We use the Kullback-Leibler divergence to measure the distance between distributions and discuss the properties of the semantic privacy. We also study the relationship between t-closeness with KL-divergence and semantic privacy, and show that t-closeness with KL-divergence and δ-disclosure privacy satisfy the generalization property and the subset property, which entail us to use the Incognito algorithm. Experiments demonstrate the efficiency and effectiveness of our approaches.