Skip to main content
SpringerLink
Log in
Book cover

Privacy and Anonymity in Information Management Systems pp 7–27Cite as

Advanced Privacy-Preserving Data Management and Analysis

  • Chapter
  • First Online:

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

The collection of large amounts of users’ sensible data by services providers such as Google, Yahoo, or Facebook poses several relevant and challenging issues. A particularly relevant problem is how to ensure a suitable degree of statistical analysis over such data without disrupting user privacy. Toward this end, in the past few years several anonymization and privacy-preserving data mining techniques have been proposed. In this work, we propose a survey of such methodologies and techniques with a particular focus on advanced topics, such as privacy preserving management of time-varying anonymized data and privacy-preserving data mining over distributed data.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Agrawal R. and Srikant R. Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, 12–15 September, Santiago, Chile, pp. 487–499, 1994.

    Google Scholar 

  2. Agrawal R. and Srikant R. Privacy preserving data mining. In: Proceedings of the ACM SIGMOD Conference, Dallas, Texas, USA, 2000.

    Google Scholar 

  3. Atzori M., Bonchi F., Giannotti F., and Pedreschi D. Blocking anonymity threats raised by frequent itemset mining. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27–30 November 2005, Houston, Texas, USA, IEEE Computer Society, Washington, DC, pp. 561–564, 2005.

    Google Scholar 

  4. Atzori M., Bonchi F., Giannotti F., and Pedreschi D. k-anonymous patterns. In: Knowledge Discovery in Databases: PKDD 2005, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3–7, 2005, Proceedings, Lecture Notes in Computer Science, Springer, Berlin, pp. 10–21, 2005.

    Google Scholar 

  5. Bellare M. and Rogaway P. Random oracles are practical: A paradigm for designing efficient protocols. In: Proceedings of the First ACM Conference on Computer and Communications Security, Fairfax, Virginia, USA, pp. 62–73, 1993.

    Google Scholar 

  6. Byun J.W., Li T., Bertino E., Li N., and Sohn Y. Privacy-preserving incremental data dissemination. Journal of Computer Security 17(1):43–68, 2009.

    Google Scholar 

  7. Cao J., Carminati B., Ferrari E., and Tan K.L. Castle: A delay-constrained scheme for k-anonymizing data streams. In: Proceedings of IEEE ICED Conference, Penang, Malaysia, 2008.

    Google Scholar 

  8. Cheung D.W-L., Han J., Ng V., Fu A.W-C., and Fu Y. A fast distributed algorithm for mining association rules. In: Proceedings of the 1996 International Conference on Parallel and Distributed Information Systems (PDIS’96), December 1996, Miami Beach, Florida, USA, IEEE, pp. 31–42.

    Google Scholar 

  9. Chin F. and Ozsoyoglu G. Auditing for secure statistical databases. In: Proceedings of the ACM’81 Conference, Los Angeles, California, USA, 1981.

    Google Scholar 

  10. Fienberg S. and McIntyre J. Data swapping: Variations on a theme by Dalenius and Reiss. In: Proceedings of Privacy in Statistical Databases, Barcelona, Spain, 2004.

    Google Scholar 

  11. Freedman M.J., Nissim K., and Pinkas B. Efficient private matching and set intersection. In: Eurocrypt 2004, Interlaken, Switzerland, 2–6 May. International Association for Cryptologic Research (IACR), Interlaken, Switzerland, 2004.

    Google Scholar 

  12. Fung B.C.M., Wang K., Fu A.W.C., Pei J. Anonymity for continuous data publishing. In: Proceedings of EDBT, ACM, ACM International Conference Proceeding Series, Nantes, France, vol. 261, pp. 264–275, 2008.

    Google Scholar 

  13. Goethals B., Laur S., Lipmaa H., and Mielikainen T. On secure scalar product computation for privacy-preserving data mining. In: The 7th Annual International Conference in Information Security and Cryptology (ICISC 2004), Seoul, Korea, 2–3 December (eds. C. Park and S. Chee), pp. 104–120, 2004.

    Google Scholar 

  14. Goldreich O., Micali S., and Wigderson A. Proofs that yield nothing but their validity or all languages in NP have zero-knowledge proof systems. Journal of the ACM, 38:690–728, 1991.

    Article  MathSciNet  Google Scholar 

  15. Goldwasser S., Micali S., and Rackoff C. The knowledge complexity of interactive proof systems. In: Proceedings of the 17th Annual ACM Symposium on Theory of Computing, Providence, Rhode Island, USA, 6–8 May, pp. 291–304, 1985.

    Google Scholar 

  16. Jagannathan G., Pillaipakkamnatt K., and Wright R.N. A new privacy preserving distributed k-clustering algorithm. In: Proceedings of the 2006 SIAM International Conference on Data Mining (SDM06), Bethesda, Maryland, USA, 2006.

    Google Scholar 

  17. Jagannathan G. and Wright R.N. Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: Proceedings of the 2005 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, 21–24 August, pp. 593–599, 2005.

    Google Scholar 

  18. Jiang W. and Atzori M. Secure distributed k-anonymous pattern mining. In: Sixth IEEE International Conference on Data Mining (ICDM06), Hong Kong, China, 18–22 December, pp. 319–329, 2006.

    Google Scholar 

  19. Jiang W. and Clifton C. A secure distributed framework for achieving k-anonymity. Special Issue of the VLDB Journal on Privacy-Preserving Data Management, September 2006.

    Google Scholar 

  20. Jiang W. and Clifton C. AC-framework for privacy-preserving collaboration. In: SIAM International Conference on Data Mining, Minneapolis, Minnesota, 26–28 April, 2007.

    Google Scholar 

  21. Jiang W., Clifton C., and Kantarcioglu M. Transforming semi-honest protocols to ensure accountability. Data and Knowledge Engineering, 65(1):57–74, 2008.

    Article  Google Scholar 

  22. Johnson W. and Lindenstrauss J. Extensions of Lipshitz mapping into Hilbert space. Contemporary Mathematics, 26:189–206, 1984.

    Article  MathSciNet  MATH  Google Scholar 

  23. Han Y., Pei J., Jiang B., Tao Y., and Jia Y. Continuous privacy preserving publishing of data streams. In: Proceedings of EDBT, ACM, ACM International Conference Proceeding Series, Saint-Petersburg, Russia, vol. 360, pp. 648–659, 2009.

    Google Scholar 

  24. Kantarcıoglu M. and Clifton C. Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Transactions on Knowledge and Data Engineering, 16(9):1026–1037, September 2004.

    Article  Google Scholar 

  25. LeFevre K, DeWitt D, and Ramakrishnan R. Incognito: Full domain k-anonymity. In: Proceedings of ACM SIGMOD Conference, Chicago, Illinois, USA, 2006.

    Google Scholar 

  26. LeFevre K., DeWitt D., and Ramakrishnan R. Mondrian multidomain k-anonymity. In: Proceedings of IEEE ICDE Conference, Istanbul, Turkey, 2007.

    Google Scholar 

  27. Li N., Li T., and Venkatasubramanian S. t-closeness: Privacy beyond k-anonymity and l-diversity. In: Proceedings of IEEE ICED Conference, 2007.

    Google Scholar 

  28. Li J., Ooi B.C., Wang W. Anonymizing streaming data for privacy protection. In: Proceedings of IEEE ICDE Conference, 2008.

    Google Scholar 

  29. Lindell Y. and Pinkas B. Privacy preserving data mining. In: Advances in Cryptology – CRYPTO 2000, Springer-Verlag, Berlin, 20–24 August, pp. 36–54, 2000.

    Google Scholar 

  30. Lindell Y. and Pinkas B. Privacy preserving data mining. Journal of Cryptology, 15(3): 177–206, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  31. Machanavajjahala A., Gehrke J., Kifer D., and Venkitasubramanian M. l-diversity: Privacy beyond k-anonymity. In: Proceedings of IEEE ICDE Conference, 2006.

    Google Scholar 

  32. Mitchell T. Machine Learning. McGraw-Hill Science/Engineering/Math, New York, NY, 1st edition, 1997.

    MATH  Google Scholar 

  33. Meyerson A. and Williams R. On the complexity of optimal k-anonymity. In: Proceedings of ACM PODS Conference, 2006.

    Google Scholar 

  34. Paillier P. Public key cryptosystems based on composite degree residuosity classes. In: Advances in Cryptology – Eurocrypt ‘99 Proceedings, Prague, Czech Republic, 2–6 May, LNCS 1592, pp. 223–238, Springer-Verlag, Berlin, 1999.

    Google Scholar 

  35. Park H. and Shim K. Approximate algorithms for k-anonymity. In: Proceedings of ACM SIGMOD Conference, Beijing, China, 2007.

    Google Scholar 

  36. Quinlan J.R. Induction of decision trees. Machine Learning, 1(1):81–106, 1986.

    Google Scholar 

  37. Schneier B. Applied Cryptography. Wiley, New York, NY, 2nd edition, 1995.

    Google Scholar 

  38. Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5):571–588, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  39. Trombetta A., Jiang W., Bertino E., Bossi L. Privately updating suppression and generalization based k-anonymous databases. In: Proceedings of ICDE, IEEE, Cancun, Mexico, pp. 1370–1372, 2008.

    Google Scholar 

  40. Vaidya J. and Clifton C. Privacy preserving association rule mining in vertically partitioned data. In: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 23–26 July, pp. 639–644, 2002.

    Google Scholar 

  41. Vaidya J. and Clifton C. Privacy-preserving k-means clustering over vertically partitioned data. In: The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, 24–27 August, pp. 206–215, 2003.

    Google Scholar 

  42. Vaidya J. and Clifton C. Privacy preserving naïve Bayes classifier for vertically partitioned data. In: 2004 SIAM International Conference on Data Mining, Lake Buena Vista, Florida, 22–24 April, pp. 522–526, 2004.

    Google Scholar 

  43. Vaidya J. and Clifton C. Privacy-preserving decision trees over vertically partitioned data. In: The 19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security, Storrs, Connecticut, 7–10 August, Springer, Berlin, 2005.

    Google Scholar 

  44. Vaidya J. and Clifton C. Secure set intersection cardinality with application to association rule mining. Journal of Computer Security, 13(4):593–622, November 2005.

    Google Scholar 

  45. Vaidya J., Clifton C., Kantarcioglu M., and Scott Patterson A. Privacy-preserving decision trees over vertically partitioned data. ACM Transactions on Knowledge Discovery in Data, 2(3):1–27, October 2008.

    Article  Google Scholar 

  46. Vaidya J., Kantarcioglu M., and Clifton C. Privacy preserving naive Bayes classification. International Journal on Very Large Data Bases, 17(4):879–898, July 2008.

    Article  Google Scholar 

  47. Vaidya J., Yu H., and Jiang X. Privacy preserving SVM classification. Knowledge and Information Systems, 14(2):161–178, February 2008.

    Article  Google Scholar 

  48. Yao C, Wang XS, Jajodia S. Checking for k-anonymity violation by views. In: Proceedings VLDB Conference, Trondheim, Norway, 2005.

    Google Scholar 

  49. Yu H., Jiang X., and Vaidya J. Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data. In: SAC ’06: Proceedings of the 2006 ACM Symposium on Applied Computing, ACM Press, New York, NY, USA, pp. 603–610, 2006.

    Google Scholar 

  50. Yu H., Vaidya J., and Jiang X. Privacy-preserving SVM classification on vertically partitioned data. In: Proceedings of PAKDD ’06, volume 3918 of Lecture Notes in Computer Science, Springer-Verlag, Berlin, January, pp. 647–656, 2006.

    Google Scholar 

  51. Wang K. and Fung B. Anonymizing sequential releases. In: Proceedings ACM KDD Conference, Philadelphia, Pennsylvania, USA, 2006.

    Google Scholar 

  52. Xiao X. and Tao Y. M-invariance: Towards privacy preserving re-publication of dynamic datasets. In: Proceeding of SIGMOD Conference, ACM, pp. 689–700, 2007.

    Google Scholar 

  53. Zhan J., Matwin S., and Chang L.W. Privacy-preserving collaborative association rule mining. In: Proceedings of the 19th Annual IFIP WG 11.3 Working Conference on Database and Applications Security, Storrs, Connecticut, 7–10 August, 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica e Comunicazione, Università degli Studi dell’Insubria, Via Mazzini 5, 21100, Varese, Italy

    Alberto Trombetta

  2. Department of Computer Science, Missouri Science and Technology University, 500 West 15th Street, Rolla, MO, USA

    Wei Jiang

  3. Department of Computer Science and CERIAS, Purdue University, 250 N. University Street, West Lafayette, IN, USA

    Elisa Bertino

Authors
  1. Alberto Trombetta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elisa Bertino
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto Trombetta .

Editor information

Editors and Affiliations

  1. Dept. Arquitectura de Computadors, Universitat Politècnica de Catalunya, Campus de la UAB s/n, Barcelona, 08034, Spain

    Jordi Nin

  2. Dept. Matematica Aplicada IV, Universitat Politècnica de Catalunya, C. Jordi Girona 1-3, Barcelona, 08034, Spain

    Javier Herranz

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer London

About this chapter

Cite this chapter

Trombetta, A., Jiang, W., Bertino, E. (2010). Advanced Privacy-Preserving Data Management and Analysis. In: Nin, J., Herranz, J. (eds) Privacy and Anonymity in Information Management Systems. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-238-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-84996-238-4_2

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-237-7

  • Online ISBN: 978-1-84996-238-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation