Advertisement

Measuring Data Quality When Applying Data Swapping and Perturbation

  • G. Canfora
  • C. A. Visaggio
Chapter

Abstract

Preserving data privacy is becoming an urgent issue to cope with. Among different technologies, the techniques of perturbation and data swapping offer many advantages, even if preliminary investigations suggest that they could deteriorate the usefulness of data. We defined a set of metrics for evaluating this drawback and carried out a case study in order to understand to which extent it is possible to enforce data security, and thus protect sensitive information, without degrading usefulness of data under unacceptable thresholds.

Keywords

Privacy Protection Data Privacy Privacy Enforcement Privacy Preference Trust Negotiation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. 1.
    Rosenblum, D. (2007) What Anyone can Know: The Privacy risks of Social Networking Sites, Security and Privacy Magazine, 5 (3): 40–49.CrossRefGoogle Scholar
  2. 2.
    Sackman, S., Struker, J. and Accorsi, R. (2006) Personalization in Privacy-Aware Highly dynamic Systems, Communications of the ACM, 49(9): 33–38.CrossRefGoogle Scholar
  3. 3.
    Langheinrich, M. (2005) Personal Privacy in Ubiquitous Computing –Tools and System Support, PhD. Dissertation, ETH Zurich.Google Scholar
  4. 4.
    Bertino, E., Ooi, B.C., Yang, Y. and Deng, R.H.(2005) Privacy and Ownership Preserving of Outsourced Medical Data, proc. 21st Int’l Conference on Data Engineering: 521–532Google Scholar
  5. 5.
    Agrawal, R., Kiernan, j., Srikant, R. and Xu, Y. (2002) Hippocratic Databases, proc. Of the 28th Int’l Conference on Very Large Database (VLDB): 143–154.Google Scholar
  6. 6.
    Platform for Privacy Preferences (P3P) Project, W3C, http://www.w3.org/P3P/ (last access on January 2007).
  7. 7.
    Maurer, U. (2004) The role of Cryptography in Database Security, proc. of SIGMOD int’l conference on Management of Data: 5–10.Google Scholar
  8. 8.
    Sweeney, L. (2002) k-Anonymity: A Model for Protecting Privacy, International Journal on Uncertainty, Fuzziness and Knowledge Based Systems, 10: 557–570.CrossRefGoogle Scholar
  9. 9.
    Li, X.B. and Sarkar, S. (2006) A Tree-Based data Perturbation Approach for Privacy – Preserving Data Mining IEEE Trans. On Software Engineering, 18(9): 1278–1283.Google Scholar
  10. 10.
    Gomatam, S. and Karr, A. (2003) Distortion Measures for Cat Egorical Data Swapping, Tech. Report 131, US Nat’l Inst. Statistical Sciences. Google Scholar
  11. 11.
    Kifer, D. and Gehrke, J. (2006) Injecting Utility into Anonymized Datasets, proc. of SIGMOD 2006: 217–228.Google Scholar
  12. 12.
    Xu, J., Wang, W., Pei, J., Wang, X., Shi, B. and Wai- Chee Fu, A. (2006) Utility-Based anonymization Using Local Recoding, proc. of KDD’06: 785–790.Google Scholar
  13. 13.
    Ghinita, G., Karras, P. , Kalnis, P. Mamoulis, N. (2007) Fast Data Anonymization with Low Information Loss, VLDB’07: 758–769.Google Scholar
  14. 14.
    Iyengar, V.S. (2002) Transforming data to satisfy privacy constraints, proc. of the 8th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining: 279–288.Google Scholar
  15. 15.
    Bayardo, R.J. and Agrawal, R. (2005) Data Privacy Thorugh Optimal k-Anonymization, proc. of 21st Int’l Conference on Data Engineering: 217–228.Google Scholar
  16. 16.
    Loukidas, G. and Shao, J. (2007) Capturing Data Usefulness and Privacy Protection in K- Anonymisation, proc. of the 2007 ACM symposium on Applied computing: 370–374.Google Scholar
  17. 17.
    Loukidas, G. and Shao, J.(2008) Data Utility and Privacy Protection Trade-Off in k-anonymisation, proc. of the 2008 International Workshop on Privacy and Anonymity in Information Society: 36–45.Google Scholar
  18. 18.
    Platform for Privacy Preferences (P3P) Project, W3C, (last access on January 2007).Google Scholar
  19. 19.
    Agrawal, R., Kiernan, J., Srikant, R. and Xu Y. (2002) Hippocratic Databases, Proc. VLDB 2002: 143–154.Google Scholar
  20. 20.
    Agrawal, R. Bird, P. Grandison, T., Kiernan, J., Logan, S. and Rjaibt, W. (2005) Extending Relational Database Systems to automatically Enforce Privacy Policy, proc. ICDE’05: 1013–1022.Google Scholar
  21. 21.
    Ashley, P., Hada, S., Karjoth, G., Powers, C. and Schunter, M. Enterprise Privacy Authorization Language (EPAL 1.1), IBM Reserach Report. (available at: http://www.zurich.ibm.com/security/enterprice-privacy/epal – last access on 19.02.07), 2003.
  22. 22.
    Pallickara, S., L., Plale, B., Fang, L. and Gannon, D. (2006) End-to-End Trustworthy Data Access in Data-Oriented Scientific Computing, proc. CCGRID’06: 4.Google Scholar
  23. 23.
    Squicciarini, A., Bertino, E., Ferrari, E., Paci, F. and Thuraisingham, B. (2007), PP-Trust- X: A System for Privacy Preserving Trust Negotiations, ACM Transactions on Information and System Security 10 (3).Google Scholar
  24. 24.
    Schlager, C., Nowey, T. and Montenegro, J., (2006) A Reference Model for Authentication and Authorisation Infrastructures Respecting Privacy and Flexibility in b2c eCommerce, proc. ARES’06: 709–716.Google Scholar
  25. 25.
    Fung, C., M., Wang, K. and Yu, S.P. (2005) Top-Down Specialization for information and Privacy Preservation, proc. ICDE’05: 206–216.Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2009

Authors and Affiliations

  1. 1.Università degli Studi del SannioBeneventoItaly

Personalised recommendations