Abstract
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.
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Hua, M., Zhang, S., Wang, W., Zhou, H., Shi, B. (2005). FMC: An Approach for Privacy Preserving OLAP. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_40
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DOI: https://doi.org/10.1007/11546849_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
Online ISBN: 978-3-540-31732-6
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