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Collusion-Resistant Processing of SQL Range Predicates

  • Manish Kesarwani
  • Akshar Kaul
  • Gagandeep Singh
  • Prasad M. Deshpande
  • Jayant R. HaritsaEmail author
Conference paper
  • 2.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

Prior solutions for securely handling SQL range predicates in outsourced cloud-resident databases have primarily focused on passive attacks in the Honest-but-Curious adversarial model, where the server is only permitted to observe the encrypted query processing. We consider here a significantly more powerful adversary, wherein the server can launch an active attack by clandestinely issuing specific range queries via collusion with a few compromised clients. The security requirement in this environment is that data values from a plaintext domain of size N should not be leaked to within an interval of size \(H\). Unfortunately, all prior encryption schemes for range predicate evaluation are easily breached with only \(O(log_2\psi )\) range queries, where \(\psi = N/H\). To address this lacuna, we present SPLIT, a new encryption scheme where the adversary requires exponentially more\(\mathbf{O}(\psi )\) – range queries to breach the interval constraint, and can therefore be easily detected by standard auditing mechanisms.

The novel aspect of SPLIT is that each value appearing in a range-sensitive column is first segmented into two parts. These segmented parts are then independently encrypted using a layered composition of a Secure Block Cipher with the Order-Preserving Encryption and Prefix-Preserving Encryption schemes, and the resulting ciphertexts are stored in separate tables. At query processing time, range predicates are rewritten into an equivalent set of table-specific sub-range predicates, and the disjoint union of their results forms the query answer. A detailed evaluation of SPLIT on benchmark database queries indicates that its execution times are well within a factor of two of the corresponding plaintext times, testifying to its efficiency in resisting active adversaries.

References

  1. 1.
    Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Order-preserving encryption for numeric data. In: Proceedings of ACM SIGMOD Conference (2004)Google Scholar
  2. 2.
    Arasu, A., Blanas, S., Eguro, K., Kaushik, R., Kossmann, D., Ramamurthy, R., Venkatesan, R.: Orthogonal security with cipherbase. In: Proceedings of CIDR Conference (2013)Google Scholar
  3. 3.
    Bajaj, S., Sion, R.: TrustedDB: a trusted hardware based outsourced database engine. PVLDB 4(12), 1359–1362 (2011)Google Scholar
  4. 4.
    Bellare, M., Ristenpart, T., Rogaway, P., Stegers, T.: Format-preserving encryption. In: Jacobson, M.J., Rijmen, V., Safavi-Naini, R. (eds.) SAC 2009. LNCS, vol. 5867, pp. 295–312. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-05445-7_19CrossRefGoogle Scholar
  5. 5.
    Boldyreva, A., Chenette, N., Lee, Y., O’Neill, A.: Order-preserving symmetric encryption. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 224–241. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01001-9_13CrossRefGoogle Scholar
  6. 6.
    Boldyreva, A., Chenette, N., O’Neill, A.: Order-preserving encryption revisited: improved security analysis and alternative solutions. In: Rogaway, P. (ed.) CRYPTO 2011. LNCS, vol. 6841, pp. 578–595. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22792-9_33CrossRefGoogle Scholar
  7. 7.
    Chi, J., Hong, C., Zhang, M., Zhang, Z.: Fast multi-dimensional range queries on encrypted cloud databases. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 559–575. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55753-3_35CrossRefGoogle Scholar
  8. 8.
    Demertzis, I., Papadopoulos, S., Papapetrou, O., Deligiannakis, A., Garofalakis, M.: Practical private range search revisited. In: Proceedings of ACM SIGMOD Conference (2016)Google Scholar
  9. 9.
    Hacigümüs, H., Iyer, B.R., Li, C., Mehrotra, S.: Executing SQL over encrypted data in the database-service-provider model. In: Proceedings of ACM SIGMOD Conference (2002)Google Scholar
  10. 10.
    Hore, B., Mehrotra, S., Tsudik, G.: A privacy-preserving index for range queries. In: Proceedings of VLDB Conference (2004)Google Scholar
  11. 11.
    Kerschbaum, F.: Frequency-hiding order-preserving encryption. In: Proceedings of CCS Conference (2015)Google Scholar
  12. 12.
    Li, J., Omiecinski, E.R.: Efficiency and security trade-off in supporting range queries on encrypted databases. In: Jajodia, S., Wijesekera, D. (eds.) DBSec 2005. LNCS, vol. 3654, pp. 69–83. Springer, Heidelberg (2005).  https://doi.org/10.1007/11535706_6CrossRefzbMATHGoogle Scholar
  13. 13.
    Li, R., Liu, A.X., Wang, A.L., Bruhadeshwar, B.: Fast range query processing with strong privacy protection for cloud computing. PVLDB 7(14), 1953–1964 (2014)Google Scholar
  14. 14.
    Popa, R.A., Li, F.H., Zeldovich, N.: An ideal-security protocol for order-preserving encoding. In: Proceedings of IEEE Symposium on Security and Privacy (2013)Google Scholar
  15. 15.
    Popa, R.A., Redfield, C.M.S., Zeldovich, N., Balakrishnan, H.: CryptDB processing queries on an encrypted database. Commun. ACM 55(9), 103–111 (2012)CrossRefGoogle Scholar
  16. 16.
    Tu, S., Kaashoek, M.F., Madden, S., Zeldovich, N.: Processing analytical queries over encrypted data. PVLDB 6(5), 289–300 (2013)Google Scholar
  17. 17.
    Wong, W.K., Kao, B., Cheung, D.W., Li, R., Yiu, S.: Secure query processing with data interoperability in a cloud database environment. In: Proceedings of ACM SIGMOD Conference (2014)Google Scholar
  18. 18.
    Xu, J., Fan, J., Ammar, M.H., Moon, A.B.: Prefix-preserving IP address anonymization: measurement-based security evaluation and a new cryptography-based scheme. In: Proceedings of ICNP Conference (2002)Google Scholar
  19. 19.
  20. 20.
  21. 21.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Manish Kesarwani
    • 1
  • Akshar Kaul
    • 1
  • Gagandeep Singh
    • 1
  • Prasad M. Deshpande
    • 2
  • Jayant R. Haritsa
    • 3
    Email author
  1. 1.IBM India Research LabBangaloreIndia
  2. 2.KENA LabsNew DelhiIndia
  3. 3.Indian Institute of ScienceBangaloreIndia

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