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
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
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
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
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Decomposition: Privacy Preservation for Multiple Sensitive Attributes
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
| Hi-index | 0.00 |
To reduce the risk of privacy disclosure during personal data publishing, the approach of anonymization is widely employed. On this topic, current studies mainly focus on two directions: (1)developing privacy preserving models which satisfy certain constraints, such as k-anonymity, l-diversity, etc.; (2)designing algorithms for certain privacy preserving model to achieve better privacy protection as well as less information loss. This paper generally belongs to the second class. We introduce an effective algorithm "BSGI" for the widely accepted privacy preserving model: l-diversity. In the meantime, we propose a novel interpretation of l-diversity: Unique Distinct l-diversity, which can be properly achieved by BSGI. We substantiate it's a stronger l-diversity model than other interpretations. Related to the algorithm, we conduct the first research on the optimal assignment of parameter laccording to certain dataset. Extensive experimental evaluation shows that Unique Distinct l-diversity provides much better protection than conventional l-diversity models, and BSGIgreatly outperforms the state of the art in terms of both efficiency and data quality.