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
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
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
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Towards trajectory anonymization: a generalization-based approach
SPRINGL '08 Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS
Towards Trajectory Anonymization: a Generalization-Based Approach
Transactions on Data Privacy
Fast clustering-based anonymization approaches with time constraints for data streams
Knowledge-Based Systems
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
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Sharing microdata tables is a primary concern in today information society. Privacy issues can be an obstacle to the free flow of such information. In recent years, disclosure control techniques have been developed to modify microdata tables in order to be anonymous. The k-anonymity framework has been widely adopted as a standard technique to remove links between public available identifiers (such as full names) and sensitive data contained in the shared tables. In this paper we give a weaker definition of k-anonymity, allowing lower distortion on the anonymized data. We show that, under the hypothesis in which the adversary is not sure a priori about the presence of a person in the table, the privacy properties of k-anonymity are respected also in the weak k-anonymity framework. Experiments on real-world data show that our approach outperforms k-anonymity in terms of distortion introduced in the released data by the algorithms to enforce anonymity.