Cardinality-based inference control in OLAP systems: An information theoretic approach

Nan Zhang, Wei Zhao, Jianer Chen

Research output: Contribution to conferencePaper

16 Scopus citations

Abstract

We address the inference control problem in data cubes with some data known to users through external knowledge. The goal of inference controls is to prevent exact values of sensitive data from being inferred through answers to online analytical processing (OLAP) queries. We present an information theoretic approach for cardinality-based inference control, which simply counts the number of cells that all queries have covered thus far to determine whether a new query should be answered. Compared to previous approaches in sum-only data cubes, our new approach has a more general framework (applies to MIN, MAX and SUM) and is more effective.

Original languageEnglish (US)
Pages59-64
Number of pages6
StatePublished - Dec 1 2004
EventDOLAP 2004: Proceedings of the Seventh ACM International Workshop on Data Warehousing and OLAP - Washington, DC, United States
Duration: Nov 12 2004Nov 13 2004

Other

OtherDOLAP 2004: Proceedings of the Seventh ACM International Workshop on Data Warehousing and OLAP
CountryUnited States
CityWashington, DC
Period11/12/0411/13/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Zhang, N., Zhao, W., & Chen, J. (2004). Cardinality-based inference control in OLAP systems: An information theoretic approach. 59-64. Paper presented at DOLAP 2004: Proceedings of the Seventh ACM International Workshop on Data Warehousing and OLAP, Washington, DC, United States.