Query Racing: Fast Completeness Certification of Query Results

  • Bernardo Palazzi
  • Maurizio Pizzonia
  • Stefano Pucacco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6166)


We present a general and effective method to certify completeness of query results on relational tables stored in an untrusted DBMS. Our main contribution is the concept of “Query Race”: we split up a general query into several single attribute queries, and exploit concurrency and speed to bind the complexity to the fastest of them. Our method supports selection queries with general composition of conjunctive and disjunctive order-based conditions on different attributes at the same time. To achieve our results, we require neither previous knowledge of queries nor specific support by the DBMS.

We validate our approach with experimental results performed on a prototypical implementation.


Query Result Search Path Disjunctive Normal Form Relational Table Basic Query 
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.


  1. 1.
    Devanbu, P.T., Gertz, M., Martel, C.U., Stubblebine, S.G.: Authentic third-party data publication. In: DBSEC, pp. 101–112 (2001)Google Scholar
  2. 2.
    Di Battista, G., Palazzi, B.: Authenticated relational tables and authenticated skip lists. In: DBSEC, pp. 31–46 (2007)Google Scholar
  3. 3.
    Miklau, G., Suciu, D.: Implementing a tamper-evident database system. In: ASIAN: 10th Asian Computing Science Conference, pp. 28–48 (2005)Google Scholar
  4. 4.
    Pang, H., Jain, A., Ramamritham, K., Tan, K.: Verifying completeness of relational query results in data publishing. In: SIGMOD Conf., pp. 407–418 (2005)Google Scholar
  5. 5.
    Pang, H., Tan, K.L.: Authenticating query results in edge computing. In: Proc. of the 20th Int. Conference on Data Engineering, pp. 560–571 (2004)Google Scholar
  6. 6.
    Xie, M., Wang, H., Yin, J., Meng, X.: Integrity auditing of outsourced data. In: VLDB, pp. 782–793 (2007)Google Scholar
  7. 7.
    Xie, M., Wang, H., Yin, J., Meng, X.: Providing freshness guarantees for outsourced databases. In: EDBT, pp. 323–332. ACM, New York (2008)Google Scholar
  8. 8.
    Mykletun, E., Narasimha, M., Tsudik, G.: Authentication and integrity in outsourced databases. Trans. Storage 2(2), 107–138 (2006)CrossRefGoogle Scholar
  9. 9.
    Tamassia, R.: Authenticated data structures. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 2–5. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Merkle, R.C.: A certified digital signature. In: Brassard, G. (ed.) CRYPTO 1989. LNCS, vol. 435, pp. 218–238. Springer, Heidelberg (1990)Google Scholar
  11. 11.
    Goodrich, M.T., Tamassia, R., Schwerin, A.: Implementation of an authenticated dictionary with skip lists and commutative hashing. In: Proc. DISCEX II, pp. 68–82 (2001)Google Scholar
  12. 12.
    Buldas, A., Roos, M., Willemson, J.: Undeniable replies for database queries. In: Proc. Intern. Baltic Conf. on DB and IS, vol. 2, pp. 215–226 (2002)Google Scholar
  13. 13.
    Goodrich, M.T., Tamassia, R., Triandopoulos, N.: Super-efficient verification of dynamic outsourced databases. In: Malkin, T.G. (ed.) CT-RSA 2008. LNCS, vol. 4964, pp. 407–424. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Devanbu, P., Gertz, M., Martel, C., Stubblebine, S.G.: Authentic data publication over the Internet. Journal of Computer Security 11(3), 291–314 (2003)Google Scholar
  15. 15.
    Singh, S., Prabhakar, S.: Ensuring correctness over untrusted private database. In: EDBT 2008, pp. 476–486. ACM, New York (2008)Google Scholar
  16. 16.
    Yang, Y., Papadias, D., Papadopoulos, S., Kalnis, P.: Authenticated join processing in outsourced databases. In: SIGMOD 2009, pp. 5–18. ACM, New York (2009)CrossRefGoogle Scholar
  17. 17.
    Zhou, Y., Salehi, A., Aberer, K.: Scalable delivery of stream query results. PVLDB 2(1), 49–60 (2009)Google Scholar
  18. 18.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Communications of the ACM 13, 422–426 (1970)zbMATHCrossRefGoogle Scholar
  19. 19.
    Cheng, W., Tan, K.: Authenticating knn query results in data publishing. In: Proc. 4th Int. Workshop on Secure Data Management, pp. 47–63 (2007)Google Scholar
  20. 20.
    Li, F., Hadjieleftheriou, M., Kollios, G., Reyzin, L.: Dynamic authenticated index structures for outsourced databases. In: ACM SIGMOD, pp. 121–132 (2006)Google Scholar
  21. 21.
    Dang, T.K.: Ensuring correctness, completeness, and freshness for outsourced tree-indexed data. Information Resources Management Jrnl., 59–76 (2008)Google Scholar
  22. 22.
    Narasimha, M., Tsudik, G.: Authentication of outsourced databases using signature aggregation and chaining. In: Li Lee, M., Tan, K.-L., Wuwongse, V. (eds.) DASFAA 2006. LNCS, vol. 3882, pp. 420–436. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  23. 23.
    Yang, Y., Papadopoulos, S., Papadias, D., Kollios, G.: Spatial outsourcing for location-based services. In: ICDE, pp. 1082–1091 (2008)Google Scholar
  24. 24.
    Polivy, D.J., Tamassia, R.: Authenticating distributed data using Web services and XML signatures. In: Proc. ACM Workshop on XML Security (2002)Google Scholar
  25. 25.
    Pugh, W.: Skip lists: A probabilistic alternative to balanced trees. In: Workshop on Algorithms and Data Structures, pp. 437–449 (1989)Google Scholar
  26. 26.
    UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2007),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bernardo Palazzi
    • 1
    • 2
  • Maurizio Pizzonia
    • 1
  • Stefano Pucacco
    • 1
  1. 1.Roma TRE UniversityRomeItaly
  2. 2.Department of Computer ScienceBrown UniversityProvidenceUSA

Personalised recommendations