Cardinality-based inference control in data cubes

  title={Cardinality-based inference control in data cubes},
  author={Lingyu Wang and Duminda Wijesekera and Sushil Jajodia},
  journal={Journal of Computer Security},
This paper addresses the inference problem in on-line analytical processing (OLAP) systems. The inference problem occurs when the exact values of sensitive attributes can be determined through answers to OLAP queries. Most existing inference control methods are computationally expensive for OLAP systems, because they ignore the special structures of OLAP queries. By exploiting such structures, we derive cardinality-based sufficient conditions for safe OLAP data cubes. Specifically, data cubes… CONTINUE READING
Highly Cited
This paper has 40 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 23 extracted citations

Privacy-Preserving OLAP: An Information-Theoretic Approach

IEEE Transactions on Knowledge and Data Engineering • 2011
View 10 Excerpts
Highly Influenced

Privacy Preserving OLAP over Distributed XML Documents

2009 International Conference on Parallel Processing Workshops • 2009
View 4 Excerpts
Highly Influenced

From Star Schemas to Big Data: 20+ Years of Data Warehouse Research

A Comprehensive Guide Through the Italian Database Research • 2018
View 1 Excerpt

PICM: A practical inference control model for protecting OLAP cubes

2015 2nd World Symposium on Web Applications and Networking (WSWAN) • 2015
View 1 Excerpt

On syntactic anonymity and differential privacy

2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW) • 2013


Publications referenced by this paper.
Showing 1-10 of 40 references

Similar Papers

Loading similar papers…