Advertisement

Framework for Efficient Search and Statistics Computation on Encrypted Cloud Data

  • Sanjit Chatterjee
  • Sayantan Mukherjee
Conference paper
  • 643 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8639)

Abstract

Storing data in untrusted cloud, keeping it confidential and allowing search and other operations on the encrypted data without revealing any meaningful information is currently an area of major interest in cryptology. Various new encryption paradigms are proposed to address the problem. Hidden Vector Encryption (HVE) is one such approach that supports different kinds of queries (e.g. search, comparison etc.) as a conjunctive normal form. In this paper we present a framework for efficient searching and computing simple statistics on the encrypted database based on the functionality of HVE. We revisit the work of Tseng et al. from IWSEC’13 and identify certain limitations of their methodology. In this paper we propose several new encodings for different attribute classes that overcome the limitations of the previous work to a great extent. Our technique, not only increases the span of queries allowed but also improves the efficiency of most of the queries than in the work of Tseng et al.

Keywords

database privacy search on encrypted data hidden vector encryption efficient encoding for searchable queries 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Boneh, D., Franklin, M.: Identity-based encryption from the weil pairing. In: Kilian, J. (ed.) CRYPTO 2001. LNCS, vol. 2139, pp. 213–229. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Boneh, D., Di Crescenzo, G., Ostrovsky, R., Persiano, G.: Public key encryption with keyword search. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 506–522. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Goyal, V., Pandey, O., Sahai, A., Waters, B.: Attribute-based encryption for fine-grained access control of encrypted data. In: Proceedings of the 13th ACM Conference on Computer and Communications Security, pp. 89–98. ACM (2006)Google Scholar
  4. 4.
    Boneh, D., Waters, B.: Conjunctive, subset, and range queries on encrypted data. In: Vadhan, S.P. (ed.) TCC 2007. LNCS, vol. 4392, pp. 535–554. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Iovino, V., Persiano, G.: Hidden-vector encryption with groups of prime order. In: Galbraith, S.D., Paterson, K.G. (eds.) Pairing 2008. LNCS, vol. 5209, pp. 75–88. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Blundo, C., Iovino, V., Persiano, G.: Private-key hidden vector encryption with key confidentiality. In: Garay, J.A., Miyaji, A., Otsuka, A. (eds.) CANS 2009. LNCS, vol. 5888, pp. 259–277. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Tseng, F.-K., Liu, Y.-H., Chen, R.-J., Lin, B.-S.P.: Statistics on encrypted cloud data. In: Sakiyama, K., Terada, M. (eds.) IWSEC 2013. LNCS, vol. 8231, pp. 133–150. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Elmasri, R.A., Navathe, S.B.: Fundamentals of Database Systems, 3rd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sanjit Chatterjee
    • 1
  • Sayantan Mukherjee
    • 1
  1. 1.Department of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

Personalised recommendations