Controlled rounding for tables with subtotals
Annals of Operations Research
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
ACM Transactions on Database Systems (TODS)
Secure databases: protection against user influence
ACM Transactions on Database Systems (TODS)
A security machanism for statistical database
ACM Transactions on Database Systems (TODS)
A General Additive Data Perturbation Method for Database Security
Management Science
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Partial cell suppression: A new methodology for statistical disclosure control
Statistics and Computing
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
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
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
Preserving confidentiality of high-dimensional tabulated data: Statistical and computational issues
Statistics and Computing
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Solving the Cell Suppression Problem on Tabular Data with Linear Constraints
Management Science
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Checking for k-anonymity violation by views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Practical Inference Control for Data Cubes (Extended Abstract)
SP '06 Proceedings of the 2006 IEEE Symposium on Security and Privacy
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
Preventing interval-based inference by random data perturbation
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
Disclosure risk in dynamic two-dimensional contingency tables (extended abstract)
ICISS'06 Proceedings of the Second international conference on Information Systems Security
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Disclosure analysis in two-way contingency tables is important in categorical data analysis. The disclosure analysis concerns whether a data snooper can infer any protected cell values, which contain privacy sensitive information, from available marginal totals (i.e., row sums and column sums) in a two-way contingency table. Previous research has been targeted on this problem from various perspectives. However, there is a lack of systematic definitions on the disclosure of cell values. Also, no previous study has been focused on the distribution of the cells that are subject to various types of disclosure. In this paper, we define four types of possible disclosure based on the exact upper bound and/or the lower bound of each cell that can be computed from the marginal totals. For each type of disclosure, we discover the distribution pattern of the cells subject to disclosure. Based on the distribution patterns discovered, we can speed up the search for all cells subject to disclosure.