Abstract
This paper addresses the inference problems in data warehouses and decision support systems such as on-line analytical processing (OLAP) systems. Even though OLAP systems restrict user accesses to predefined aggregations, inappropriate disclosure of sensitive attribute values may still occur. Based on a definition of non-compromiseability to mean that any member of a set of variables satisfying a given set of their aggregations can have more than one value, we derive sufficient conditions for non-compromiseability in sum-only data cubes. Under this definition, (1) the non-compromiseability of multi-dimensional aggregations can be reduced to that of one dimensional aggregations, (2) full or dense core cuboids are non-compromiseable, and (3) there is a tight lower bound for the cardinality of a core cuboid to remain non-compromiseable. Based on these results, taken together with a three-tier model for controlling inferences, we provide a divide-and-conquer algorithm that uniformly divides data sets into chunks and builds a data cube on each such chunk. The union of these data cubes are then used to provide users with inference-free OLAP queries.
This work was partially supported by the National Science Foundation under grant CCR-0113515.
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Wang, L., Wijesekera, D., Jajodia, S. (2002). Cardinality-Based Inference Control in Sum-Only Data Cubes. In: Gollmann, D., Karjoth, G., Waidner, M. (eds) Computer Security — ESORICS 2002. ESORICS 2002. Lecture Notes in Computer Science, vol 2502. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45853-0_4
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