Views for Multilevel Database Security
IEEE Transactions on Software Engineering - Special issue on computer security and privacy
A unified framework for enforcing multiple access control policies
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(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
Privacy in Statistical Databases: CASC Project International Workshop, PSD 2004, Barcelona, Spain, June 9-11, 2004, Proceedings (Lecture Notes in Computer Science)
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Maintaining K-Anonymity against Incremental Updates
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Continuous privacy preserving publishing of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Privacy-preserving incremental data dissemination
Journal of Computer Security - Selected papers from the Third and Fourth Secure Data Management (SDM) workshops
Efficient Table Anonymization for Aggregate Query Answering
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Incremental privacy preservation for associative classification
Proceedings of the ACM first international workshop on Privacy and anonymity for very large databases
Closeness: A New Privacy Measure for Data Publishing
IEEE Transactions on Knowledge and Data Engineering
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The emerging of the internet-based services poses a privacy threat to the individuals. Data transformation to meet a privacy standard becomes a requirement for typical data processing for the services. k, e-anonymisation is one of the most promising data transformation approaches, since it can provide high-accuracy aggregate query results. Though, the computational cost of the algorithm providing optimal solutions for such approach is not very high, i.e., On². In certain environments, the data to be processed can be appended at any time. In this paper, we address an efficiency issue of the incremental privacy preservation using k, e-anonymisation approach. The impact of the increment is observed theoretically. We propose an incremental algorithm based on such observation. The algorithm can replace the quadratic-complexity processing by a linear function on some part of the dataset, while the optimal results are guaranteed. Additionally, a few indexes are proposed to further improve the efficiency of the proposed algorithm. The experiments have been conducted to validate our work. From the results, it can be seen that the proposed work is highly efficient comparing with the non-incremental algorithm and an approximation algorithm.