Secure statistical databases with random sample queries
ACM Transactions on Database Systems (TODS)
Cardinality-Based Inference Control in Sum-Only Data Cubes
ESORICS '02 Proceedings of the 7th European Symposium on Research in Computer Security
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
A Robust Sampling-Based Framework for Privacy Preserving OLAP
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
A secure multiparty computation privacy preserving OLAP framework over distributed XML data
Proceedings of the 2010 ACM Symposium on Applied Computing
Balancing accuracy and privacy of OLAP aggregations on data cubes
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
Journal of Computer and System Sciences
Towards a theory for privacy preserving distributed OLAP
Proceedings of the 2012 Joint EDBT/ICDT Workshops
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To preserve private information while providing thorough analysis is one of the significant issues in OLAP systems. One of the challenges in it is to prevent inferring the sensitive value through the more aggregated non-sensitive data. This paper presents a novel algorithm FMC to eliminate the inference problem by hiding additional data besides the sensitive information itself, and proves that this additional information is both necessary and sufficient. Thus, this approach could provide as much information as possible for users, as well as preserve the security. The strategy does not impact on the online performance of the OLAP system. Systematic analysis and experimental comparison are provided to show the effectiveness and feasibility of FMC.