BMPD statistical software manual
BMPD statistical software manual
A universal table model for categorical databases
Information Sciences: an International Journal
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
PODS '91 Proceedings of the tenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A universal-scheme approach to statistical databases containing homogeneous summary tables
ACM Transactions on Database Systems (TODS)
Cryptography and data security
Cryptography and data security
Query Evaluability in Statistical Databases
IEEE Transactions on Knowledge and Data Engineering
Multidimensional databases
Privacy in multidimensional databases
Multidimensional databases
Sanitization models and their limitations
NSPW '06 Proceedings of the 2006 workshop on New security paradigms
Statistical confidentiality: Optimization techniques to protect tables
Computers and Operations Research
A statistical metadata model for clinical trials' data management
Computer Methods and Programs in Biomedicine
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Safeguarding confidential information is of paramount concern to government agencies in publishing statistical data. Given a set of sensitive cells, the problem is to identify a set of complementary cells to suppress so as to mask the values of the sensitive cells. All of the existing cell suppression methods fail to consider the relationships among cell values and the representation of these relationships in marginal totals. That marginal totals may contain potent information has not been appreciated. This paper employs the theory of nominal data analysis to demonstrate that the disclosure of marginal totals can be very risky. It recommends adding a front-end test to the existing methods. The goal is to identify a list of sensitive marginal totals that have to be suppressed. This increases the sophistication of cell suppression methodology by providing an extra layer of protection.