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
Low Power Technology Mapping for LUT based FPGA "A Genetic Algorithm Approach"
VLSID '03 Proceedings of the 16th International Conference on VLSI Design
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
Blocking Anonymity Threats Raised by Frequent Itemset Mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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
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
A crossover operator for the k- anonymity problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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 for privacy preservation with less information loss
ACM SIGKDD Explorations Newsletter
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
ICDT'05 Proceedings of the 10th international conference on Database Theory
Privacy-preserving distributed k-anonymity
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
A genetic algorithm based approach to route selection and capacity flow assignment
Computer Communications
Fixed-Parameter Tractability of Anonymizing Data by Suppressing Entries
COCOA 2008 Proceedings of the 2nd international conference on Combinatorial Optimization and Applications
Secure construction of k-unlinkable patient records from distributed providers
Artificial Intelligence in Medicine
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As more and more person-specific data like health information becomes available, increasing attention is paid to confidentiality and privacy protection. One proposed model of privacy protection is k- Anonymity, where a dataset is k-anonymous if each record is identical to at least (k-1) others in the dataset. Our goal is to minimize information loss while transforming a collection of records to satisfy the k-Anonymity model. The downside to current greedy anonymization algorithms is their potential to get stuck at poor local optimums. In this paper, we propose an Ordered Greed Framework for k-Anonymity. Using our framework, designers can avoid the poor-local-optimum problem by adding stochastic elements to their greedy algorithms. Our preliminary experimental results indicate improvements in both runtime and solution quality. We also discover a surprising result concerning at least two widely-accepted greedy optimization algorithms in the literature. More specifically, for anonymization algorithms that process datasets in column-wise order, we show that a random column ordering can lead to significantly higher quality solutions than orderings determined by known greedy heuristics.