Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
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
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
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
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
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
Utility-based anonymization for privacy preservation with less information loss
ACM SIGKDD Explorations Newsletter
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
An efficient clustering method for k-anonymization
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Towards Preference-Constrained k-Anonymisation
Database Systems for Advanced Applications
Achieving k-anonymity via a density-based clustering method
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Towards a Common Notion of Privacy Leakage on Public Database
BWCCA '10 Proceedings of the 2010 International Conference on Broadband, Wireless Computing, Communication and Applications
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
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
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A challenging task in privacy protection for public data is to realize an algorithm that generalizes a table according to a user's requirement. In this paper, we propose an anonymization scheme for generating a k-anonymous table, and show evaluation results using three different tables. Our scheme is based on full-domain generalization and the requirements are automatically incorporated into the generated table. The scheme calculates the scores of intermediate tables based on user-defined priorities for attributes and selects a table suitable for the user's requirements. Thus, the generated table meets user's requirements and is employed in the services provided by users without any modification or evaluation.