Advertisement

k-Anonymity-Based Horizontal Fragmentation to Preserve Privacy in Data Outsourcing

  • Abbas Taheri Soodejani
  • Mohammad Ali Hadavi
  • Rasool Jalili
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7371)

Abstract

This paper proposes a horizontal fragmentation method to preserve privacy in data outsourcing. The basic idea is to identify sensitive tuples, anonymize them based on a privacy model and store them at the external server. The remaining non-sensitive tuples are also stored at the server side. While our method departs from using encryption, it outsources all the data to the server; the two important goals that existing methods are unable to achieve simultaneously. The main application of the method is for scenarios where encrypting or not outsourcing sensitive data may not guarantee the privacy.

Keywords

Data outsourcing privacy horizontal fragmentation k-anonymity 

References

  1. 1.
    Ciriani, V., De Capitani di Vimercati, S., Foresti, S., Jajodia, S., Paraboschi, S., Samarati, P.: Enforcing Confidentiality Constraints on Sensitive Databases with Lightweight Trusted Clients. In: Gudes, E., Vaidya, J. (eds.) Data and Applications Security 2009. LNCS, vol. 5645, pp. 225–239. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Wiese, L.: Horizontal Fragmentation for Data Outsourcing with Formula-Based Confidentiality Constraints. In: Echizen, I., Kunihiro, N., Sasaki, R. (eds.) IWSEC 2010. LNCS, vol. 6434, pp. 101–116. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Ciriani, V., De Capitani di Vimercati, S., Foresti, S., Jajodia, S., Paraboschi, S., Samarati, P.: Keep a Few: Outsourcing Data While Maintaining Confidentiality. In: Backes, M., Ning, P. (eds.) ESORICS 2009. LNCS, vol. 5789, pp. 440–455. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Ciriani, V., De Capitani di Vimercati, S., Foresti, S., Jajodia, S., Paraboschi, S., Samarati, P.: Combining fragmentation and encryption to protect privacy in data storage. ACM Transactions on Information and System Security 13, 1–33 (2010)CrossRefGoogle Scholar
  5. 5.
    Aggarwal, G., Bawa, M., Ganesan, P., Garcia-molina, H., Kenthapadi, K., Motwani, R., Srivastava, U., Thomas, D., Xu, Y.: Two can keep a secret: A distributed architecture for secure database services. In: Second Biennial Conference on Innovative Data Systems Research, pp. 186–199 (2005)Google Scholar
  6. 6.
    Foresti, S.: Preserving privacy in data outsourcing. Springer-Verlag New York Inc. (2011)Google Scholar
  7. 7.
    Beeri, C., Vardi, M.Y.: A Proof Procedure for Data Dependencies. J. ACM 31, 718–741 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Maier, D., Mendelzon, A.O., Sagiv, Y.: Testing implications of data dependencies. ACM Trans. Database Syst. 4, 455–469 (1979)CrossRefGoogle Scholar
  9. 9.
    Fagin, R., Kolaitis, P.G., Miller, R.J., Popa, L.: Data exchange: semantics and query answering. Theoretical Computer Science 336, 89–124 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information (abstract). In: Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, p. 188. ACM, Seattle (1998)CrossRefGoogle Scholar
  11. 11.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1, 3 (2007)CrossRefGoogle Scholar
  12. 12.
    Ninghui, L., Tiancheng, L., Venkatasubramanian, S.: t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. In: 23rd International Conference on Data Engineering, pp. 106–115 (2007)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Abbas Taheri Soodejani
    • 1
  • Mohammad Ali Hadavi
    • 1
  • Rasool Jalili
    • 1
  1. 1.Data and Network Security Laboratory, Department of Computer EngineeringSharif University of TechnologyIran

Personalised recommendations