Practical data-swapping: the first steps
ACM Transactions on Database Systems (TODS)
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Leaderrship and group search in group decision support systems
Decision Support Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Impacts of user privacy preferences on personalized systems: a comparative study
Designing personalized user experiences in eCommerce
Some theoretical results about the computation time of evolutionary algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Editorial: intelligence and security informatics: information systems perspective
Decision Support Systems - Special issue: Intelligence and security informatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Dare to share: Protecting sensitive knowledge with data sanitization
Decision Support Systems
Minimizing Information Loss and Preserving Privacy
Management Science
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Providing k-anonymity in data mining
The VLDB Journal — The International Journal on Very Large Data Bases
Predicting going concern opinion with data mining
Decision Support Systems
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Customer relationship management using partial focus feature reduction
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
The Journal of Supercomputing
Pricing and disseminating customer data with privacy awareness
Decision Support Systems
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Identity disclosure is one of the most serious privacy concerns in today's information age. A well-known method for protecting identity disclosure is k-anonymity. A dataset provides k-anonymity protection if the information for each individual in the dataset cannot be distinguished from at least k-1 individuals whose information also appears in the dataset. There is a flaw in k-anonymity that would still allow an intruder to discern the confidential information of individuals in the anonymized data. To overcome this problem, we propose a data reconstruction approach to achieve k-anonymity protection in predictive data mining. In this approach, the potentially identifying attributes are first masked using aggregation (for numeric data) and swapping (for nominal data). A genetic algorithm technique is then applied to the masked data to find a good subset of it. This subset is then replicated to form the released dataset that satisfies the k-anonymity constraint.