Skip to main content

A Probabilistic Framework for Building Privacy-Preserving Synopses of Multi-dimensional Data

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 5069)

Abstract

The problem of summarizing multi-dimensional data into lossy synopses supporting the estimation of aggregate range queries has been deeply investigated in the last three decades. Several summarization techniques have been proposed, based on different approaches, such as histograms, wavelets and sampling. The aim of most of the works in this area was to devise techniques for constructing effective synopses, enabling range queries to be estimated, trading off the efficiency of query evaluation with the accuracy of query estimates. In this paper, the use of summarization is investigated in a more specific context, where privacy issues are taken into account. In particular, we study the problem of constructing privacy-preserving synopses, that is synopses preventing sensitive information from being extracted while supporting ‘safe’ analysis tasks. In this regard, we introduce a probabilistic framework enabling the evaluation of the quality of the estimates which can be obtained by a user owning the summary data. Based on this framework, we devise a technique for constructing histogram-based synopses of multi-dimensional data which provide as much accurate as possible answers for a given workload of ‘safe’ queries, while preventing high-quality estimates of sensitive information from being extracted.

Keywords

  • Range Query
  • Sensitive Information
  • Point Query
  • Large Data Base
  • Probabilistic Framework

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.

This work was supported by a grant from the Italian Research Project FIRB “TOCAI”, funded by MUR.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-69497-7_10
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-69497-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acharya, S., Poosala, V., Ramaswamy, S.: Selectivity estimation in spatial databases. In: Proc. of 1999 ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 1999), Philadelphia (PA), USA, June 1-3, 1999, pp. 13–24 (1999)

    Google Scholar 

  2. Bruno, N., Chaudhuri, S., Gravano, L.: STHoles: a multi-dimensional workload aware histogram. In: Proc. of 2001 ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2001), Santa Barbara (CA), USA, May 21-24, 2001, pp. 211–222 (2001)

    Google Scholar 

  3. Chakrabarti, K., Garofalakis, M.N., Rastogi, R., Shim, K.: Approximate query processing using wavelets. The VLDB Journal 10(2-3), 199–223 (2001)

    MATH  Google Scholar 

  4. Chawla, S., Dwork, C., McSherry, F., Smith, A., Wee, H.: Toward Privacy in Public Databases. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 363–385. Springer, Heidelberg (2005)

    Google Scholar 

  5. Furfaro, F., Mazzeo, G.M., Sirangelo, C.: Exploiting cluster analysis for constructing multi-dimensional histograms on both static and dynamic data. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 442–459. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  6. Garofalakis, M.N., Gibbons, P.B.: Wavelet synopses with error guarantees. In: Proc. of 2002 ACM SIGMOD Int. Conf. on Managment of Data (SIGMOD 2002), Madison (WI), USA, June 3-6, 2002, pp. 476–487 (2002)

    Google Scholar 

  7. Gibbons, P.B., Matias, Y.: New sampling-based summary statistics for improving approximate query answers. In: Proc. of 1998 ACM SIGMOD Int. Conf. on Managment of Data (SIGMOD 1998), Seattle (WA), USA, June 2-4, pp. 331–342 (1998)

    Google Scholar 

  8. Guha, S., Shim, K., Woo, J.: REHIST: Relative Error Histogram Construction Algorithms. In: Proc. of 30th Int. Conf. on Very Large Data Bases (VLDB 2004), Toronto, Canada, August 29-September 30, pp. 300–311 (2004)

    Google Scholar 

  9. Gunopulos, D., Kollios, G., Tsotras, V.J., Domeniconi, C.: Selectivity estimators for multidimensional range queries over real attributes. The VLDB Journal 14(2), 137–154 (2005)

    CrossRef  Google Scholar 

  10. Jagadish, H.V., Koudas, N., Muthukrishnan, S., Poosala, V., Sevcik, K., Suel, T.: Optimal histograms with quality guarantees. In: Proc. of 24th Int. Conf. on Very Large Data Bases (VLDB 2004), New York (NY), USA, August 24-27, pp. 275–286 (2004)

    Google Scholar 

  11. Ioannidis, Y.E.: The History of Histograms (abridged). In: Proc. of 29th Int. Conf. on Very Large Data Bases (VLDB 2003), Berlin, Germany, September 9-12, pp. 19–30 (2003)

    Google Scholar 

  12. Malvestuto, F.M.: A Universal-Scheme Approach to Statistical Databases Containing Homogeneous Summary Tables. ACM Transactions on Database Systems 18(4), 678–708 (1993)

    CrossRef  Google Scholar 

  13. Malvestuto, F.M., Mezzini, M., Moscarini, M.: Auditing sum-queries to make a statistical database secure. ACM Transactions on Information and Systems Security 9(1), 31–60 (2006)

    CrossRef  MathSciNet  Google Scholar 

  14. Muthukrishnan, S., Poosala, V., Suel, T.: On Rectangular Partitioning in Two Dimensions: Algorithms, Complexity and Applications. In: Proc. 7th Int. Conf. on Database Theory (ICDT), Jerusalem, Israel, January 10-12 (1999)

    Google Scholar 

  15. Poosala, V., Ioannidis, Y.E.: Selectivity estimation without the attribute value independence assumption. In: Proc. of 23rd Int. Conf. on Very Large Data Bases (VLDB 1997), Athens, Greece, August 25-29, pp. 486–495 (1997)

    Google Scholar 

  16. Sweeney, L.: k-Anomity: A model for protecting privacy. Int. Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)

    MATH  CrossRef  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Furfaro, F., Mazzeo, G.M., Saccà, D. (2008). A Probabilistic Framework for Building Privacy-Preserving Synopses of Multi-dimensional Data. In: Ludäscher, B., Mamoulis, N. (eds) Scientific and Statistical Database Management. SSDBM 2008. Lecture Notes in Computer Science, vol 5069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69497-7_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69497-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69476-2

  • Online ISBN: 978-3-540-69497-7

  • eBook Packages: Computer ScienceComputer Science (R0)