Learning from good and bad data
Learning from good and bad data
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A modified random perturbation method for database security
ACM Transactions on Database Systems (TODS)
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Classification by feature partitioning
Machine Learning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
Inference Control in Statistical Databases, From Theory to Practice
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Re-identifying register data by survey data using cluster analysis: an empirical study
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Aggregation techniques for statistical confidentiality
Aggregation operators
Data Mining: the search for knowledge in databases.
Data Mining: the search for knowledge in databases.
Will the real iris data please stand up?
IEEE Transactions on Fuzzy Systems
Journal of Biomedical Informatics
Microdata protection through approximate microaggregation
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
An approximate microaggregation approach for microdata protection
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Statistical disclosure control (SDC) and artificial intelligence (AI) use similar tools for different purposes. This work describes the common elements of both areas to increase their synergy.SDC is a discipline that seeks to modify statistical data so that they can be published (typically by National Statistical Offices) without giving away the identity of any individual behind the data. When dealing with individual data (microdata in SDC jargon), both SDC procedures and AI knowledge integration procedures use similar principles for different purposes (masking data vs. improving its quality). Similarities can also be found for methods evaluating re-identification risk in SDC and data mining tools for making data consistent.This paper explores those methodological connections with the aim of stimulating interaction between both fields. In particular, data mining turns out to be a common interest of both fields.