Locating Encrypted Data Precisely without Leaking Their Distribution

  • Liqing Huang
  • Yi Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7418)


Data encryption is a popular solution to ensure the privacy of the data in outsourced databases. A typical strategy is to store sensitive data encrypted and map those original values into bucket tags for querying on encrypted data. To achieve computations over encrypted data, the homomorphic encryption (HE) methods are proposed. However, performing those computations needs locating data precisely. Existing test-over-encrypted-data methods cannot prevent a curious service provider doing in the same way and causing the leaks of original data distribution. In this paper, we propose a method, named Splitting-Duplicating, to support encrypted data locating precisely by introducing an auxiliary value tag. To protect the privacy of original data distribution, we limit the frequencies of different tag values in a given range. We use an entropy based metric to measure the degree of privacy protected. We have conducted some experiments to validate our proposed method.


Range Query Block Cipher Sensitive Data Encrypt Data Query Window 
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.
    Damiani, E., Vimercati, S., Jajodia, S., Paraboschi, S., Samarati, P.: Balancing Confidentiality and Efficiency in Untrusted Relational DBMSs. In: Proceedings of ACM CCS 2003, pp. 93–102 (2003)Google Scholar
  2. 2.
    Paillier, P.: Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)Google Scholar
  3. 3.
    Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of STOC 2009, pp. 169–178 (2009)Google Scholar
  4. 4.
    Hacigumus, H., Iyer, B., Li, C., Mehrotra, S.: Executing SQL over Encrypted Data in the Database-Service-Provider Model. In: Proceedings of ACM SIGMOD 2002, pp. 216–227 (2002)Google Scholar
  5. 5.
    Pagel, B., Six, H., Toben, H., Widmayer, P.: Towards an Analysis of Range Query Performance in Spatial Data Structures. In: Proceedings of PODS 1993, pp. 214–221 (1993)Google Scholar
  6. 6.
    Song, D., Wagner, D., Perrig, A.: Practical techniques for searches on encrypted data. In: Proceedings of IEEE S&P 2000, pp. 44–55 (2000)Google Scholar
  7. 7.
    Yang, G., Tan, C.H., Huang, Q., Wong, D.S.: Probabilistic Public Key Encryption with Equality Test. In: Pieprzyk, J. (ed.) CT-RSA 2010. LNCS, vol. 5985, pp. 119–131. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Popa, R., Redfield, C., Zeldovich, N., Balakrishnan, H.: CryptDB: Protecting Confidentiality with Encrypted Query Processing. In: Proceedings of SOSP 2011, pp. 85–100 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Liqing Huang
    • 1
    • 2
  • Yi Tang
    • 1
    • 2
  1. 1.School of Mathematics and Information ScienceGuangzhou UniversityGuangzhouChina
  2. 2.Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong Higher Education InstitutesGuangzhou UniversityGuangzhouChina

Personalised recommendations