Theory of linear and integer programming
Theory of linear and integer programming
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Linear programming
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)
The tracker: a threat to statistical database security
ACM Transactions on Database Systems (TODS)
Secure statistical databases with random sample queries
ACM Transactions on Database Systems (TODS)
A security machanism for statistical database
ACM Transactions on Database Systems (TODS)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
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
Security of Statistical Databases - Compromise through Attribute Correlational Modeling
Proceedings of the Second International Conference on Data Engineering
A Privacy-Enhanced Microaggregation Method
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
Computational Issues Connected with the Protection of Sensitive Statistics by Auditing Sum Queries
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Constraints, Inference Channels and Secure Databases
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
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
Cardinality-Based Inference Control in Sum-Only Data Cubes
ESORICS '02 Proceedings of the 7th European Symposium on Research in Computer Security
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Cardinality-based inference control in data cubes
Journal of Computer Security
Parity-based inference control for multi-dimensional range sum queries
Journal of Computer Security
Simulatable Binding: Beyond Simulatable Auditing
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
An efficient online auditing approach to limit private data disclosure
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
New paradigm of inference control with trusted computing
Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security
Answering queries based on imprecision and uncertainty trade-offs in numeric databases
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
Preventing range disclosure in k-anonymised data
Expert Systems with Applications: An International Journal
Denials leak information: Simulatable auditing
Journal of Computer and System Sciences
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In this paper we study the feasibility of auditing interval-based inference. Sensitive information about individuals is said to be compromised if an accurate enoughin terval, called inference interval, is obtained into which the value of the sensitive information must fall. Compared with auditing exact inference that is traditionally studied, auditing interval-based inference is more complicated. Existing auditing methods such as audit expert do not apply to this case. Our result shows that it is intractable to audit interval-based inference for bounded integer values; while for bounded real values, the auditing problem is polynomial yet involves complicated computation of mathematical programming. To further examine the practicability of auditing interval-based inference, we classify various auditing methods into three categories: exact auditing, optimistic auditing, and pessimistic auditing. We analyze the trade-offs that can be achieved by these methods among various auditing objectives: inference security, database usability, and auditing complexity.