Security checking in relational database management systems augmented with inference engines
Computers and Security
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Elements of information theory
Elements of information theory
Data Security Equals Graph Connectivity
SIAM Journal on Discrete Mathematics
Minimal data upgrading to prevent inference and association attacks
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Parsimonious downgrading and decision trees applied to the inference problem
Proceedings of the 1998 workshop on New security paradigms
Design of LDV: A Multilevel Secure Relational Database Management
IEEE Transactions on Knowledge and Data Engineering
Inference in MLS Database Systems
IEEE Transactions on Knowledge and Data Engineering
Wizard: A Database Inference Analysis and Detection System
IEEE Transactions on Knowledge and Data Engineering
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
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
Some Privacy Issues in Knowledge Discovery: The OECD Personal Privacy Guidelines
IEEE Expert: Intelligent Systems and Their Applications
Advances in Inference Control in Statistical Databases: An Overview
Inference Control in Statistical Databases, From Theory to Practice
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
Exact and approximate methods for data directed microaggregation in one or more dimensions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
k-anonymity: a model for protecting privacy
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
A Polynomial Algorithm for Optimal Univariate Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Detection and Elimination of Inference Channels in Multilevel Relational Database Systems
SP '93 Proceedings of the 1993 IEEE Symposium on Security and Privacy
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Automated cell suppression to preserve confidentiality of business statistics
SSDBM'83 Proceedings of the 2nd international workshop on Proceedings of the Second International Workshop on Statistical Database Management
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A Genetic Approach to Multivariate Microaggregation for Database Privacy
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Suppressing microdata to prevent probabilistic classification based inference
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
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The revolution of the Internet together with the progression in computer technology makes it easy for institutions to collect an unprecedented amount of personal data. This pervasive data collection rally coupled with the increasing necessity of dissemination and sharing of non-aggregated data, i.e., microdata, raised a lot of concerns about privacy. One method to ensure privacy is to selectively hide the confidential, i.e. sensitive, information before disclosure. However, with data mining techniques, it is now possible for an adversary to predict the hidden confidential information from the disclosed data sets. In this paper, we concentrate on one such data mining technique called classification. We extend our previous work on microdata suppression to prevent both probabilistic and decision tree classification based inference. We also provide experimental results showing the effectiveness of not only the proposed methods but also the hybrid methods, i.e., methods suppressing microdata against both classification models, on real-life data sets.