Proceedings of the 3rd International Conference on Genetic Algorithms
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
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
On the comparison of microdata disclosure control algorithms
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Genetic algorithm-based clustering approach for k-anonymization
Expert Systems with Applications: An International Journal
Data and Structural k-Anonymity in Social Networks
Privacy, Security, and Trust in KDD
Allowing privacy protection algorithms to jump out of local optimums: an ordered greed framework
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Privacy-aware access control with generalization boundaries
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
On-the-fly hierarchies for numerical attributes in data anonymization
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
On-the-fly generalization hierarchies for numerical attributes revisited
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
Journal of Computer Security
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Recent dissemination of personal data has created an important optimization problem: what is the minimal transformation of a dataset that is needed to guarantee the anonymity of the underlying individuals? One natural representation for this problem is a bit-string, which makes a genetic algorithm a logical choice for optimization. Unfortunately, under certain realistic conditions, not all bit combinations will represent valid solutions. This means that in many instances, useful solutions are sparse in the search space. We implement a new crossover operator that preserves valid solutions under this representation. Our results show that this reproductive strategy is more efficient, effective, and robust than previous work. We also investigate how the population size and uniqueness can affect the performance of genetic search on this application.