A Theoretically-Sound Accuracy/Privacy-Constrained Framework for Computing Privacy Preserving Data Cubes in OLAP Environments

  • Alfredo Cuzzocrea
  • Domenico Saccà
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7566)


State-of-the-art privacy preserving OLAP approaches lack of strong theoretical bases that provide solid foundations to them. In other words, there is not a theory underlying such approaches, but rather, an algorithmic vision of the problem. A class of methods that clearly confirm to us the trend above is represented by the so-called perturbation-based techniques, which propose to alter the target data cube cell-by-cell to gain privacy preserving query processing. This approach exposes us to clear limits, whose lack of extendibility and scalability are only the tip of an enormous iceberg. With the aim of fulfilling this critical drawback, in this paper we propose and experimentally assess a theoretically-sound accuracy/privacy-constrained framework for computing privacy preserving data cubes in OLAP environments. The benefits deriving from our proposed framework are two-fold. First, we provide and meaningfully exploit solid theoretical foundations to the privacy preserving OLAP problem that pursue the idea of obtaining privacy preserving data cubes via balancing accuracy and privacy of cubes by means of flexible sampling methods. Second, we ensure the efficiency and the scalability of the proposed approach, as confirmed to us by our experimental results, thanks to the idea of leaving the algorithmic vision of the privacy preserving OLAP problem.


Data Cube Privacy Preservation Privacy Preserve Query Region Aggregate Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adam, N.R., et al.: Security-Control Methods for Statistical Databases: A Comparative Study. ACM Computing Surveys 21(4), 515–556 (1989)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Agarwal, S., et al.: On the Computation of Multidimensional Aggregates. In: VLDB, pp. 506–521 (1996)Google Scholar
  3. 3.
    Agrawal, R., et al.: Privacy-Preserving OLAP. In: ACM SIGMOD, pp. 251–262 (2005)Google Scholar
  4. 4.
    Chin, F.Y., et al.: Auditing and Inference Control in Statistical Databases. IEEE Trans. on Software Engineering 8(6), 574–582 (1982)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Cuzzocrea, A.: Overcoming Limitations of Approximate Query Answering in OLAP. In: IEEE IDEAS, pp. 200–209 (2005)Google Scholar
  6. 6.
    Cuzzocrea, A.: Accuracy Control in Compressed Multidimensional Data Cubes for Quality of Answer-based OLAP Tools. In: IEEE SSDBM, pp. 301–310 (2006)Google Scholar
  7. 7.
    Cuzzocrea, A.: Improving Range-Sum Query Evaluation on Data Cubes via Polynomial Approximation. Data & Knowledge Engineering 56(2), 85–121 (2006)CrossRefGoogle Scholar
  8. 8.
    Denning, D.E., et al.: Inference Controls for Statistical Databases. IEEE Computer 16(7), 69–82 (1983)CrossRefGoogle Scholar
  9. 9.
    UCI KDD Archive, The Forest CoverType Data Set,
  10. 10.
    Gray, J., et al.: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals. Data Mining and Knowledge Discovery 1(1), 29–54 (1997)CrossRefGoogle Scholar
  11. 11.
    Han, J., et al.: Efficient Computation of Iceberg Cubes with Complex Measures. In: ACM SIGMOD, pp. 1–12 (2001)Google Scholar
  12. 12.
    Hua, M., Zhang, S., Wang, W., Zhou, H., Shi, B.-L.: FMC: An Approach for Privacy Preserving OLAP. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 408–417. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Machanavajjhala, A., et al.: L-diversity: Privacy beyond k-Anonymity. ACM Trans. on Knowledge Discovery from Data 1(1), art. no. 3 (2007)Google Scholar
  14. 14.
    Malvestuto, F.M., et al.: Auditing Sum-Queries to Make a Statistical Database Secure. ACM Trans. on Information and System Security 9(1), 31–60 (2006)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Schlorer, J.: Security of Statistical Databases: Multidimensional Transformation. ACM Trans. on Database Systems 6(1), 95–112 (1981)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Stuart, A., et al.: Kendall’s Advanced Theory of Statistics: Distribution Theory, 6th edn., vol. 1. Oxford University Press, New York City (1998)Google Scholar
  17. 17.
    Sung, S.Y., et al.: Privacy Preservation for Data Cubes. Knowledge and Information Systems 9(1), 38–61 (2006)CrossRefGoogle Scholar
  18. 18.
    Sweeney, L.: k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Transaction Processing Council, TPC Benchmark H,
  20. 20.
    Wang, L., et al.: Securing OLAP Data Cubes against Privacy Breaches. In: IEEE SSP, pp. 161–175 (2004)Google Scholar
  21. 21.
    Wang, L., et al.: Cardinality-based Inference Control in Data Cubes. Journal of Computer Security 12(5), 655–692 (2004)Google Scholar
  22. 22.
    Zhang, N., et al.: Cardinality-based Inference Control in OLAP Systems: An Information Theoretic Approach. In: ACM DOLAP, pp. 59–64 (2004)Google Scholar
  23. 23.
    Cuzzocrea, A., Saccà, D.: A Constraint-Based Framework for Computing Privacy Preserving OLAP Aggregations on Data Cubes. In: ADBIS CEUR Workshop Proceedings, vol. 789, pp. 95–106 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alfredo Cuzzocrea
    • 1
  • Domenico Saccà
    • 1
  1. 1.ICAR-CNR and University of CalabriaItaly

Personalised recommendations