skip to main content
10.1145/1989284.1989304acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

On the complexity of privacy-preserving complex event processing

Published:13 June 2011Publication History

ABSTRACT

Complex Event Processing (CEP) Systems are stream processing systems that monitor incoming event streams in search of userspecified event patterns. While CEP systems have been adopted in a variety of applications, the privacy implications of event pattern reporting mechanisms have yet to be studied - a stark contrast to the significant amount of attention that has been devoted to privacy for relational systems. In this paper we present a privacy problem that arises when the system must support desired patterns (those that should be reported if detected) and private patterns (those that should not be revealed). We formalize this problem, which we term privacy-preserving, utility maximizing CEP (PP-CEP), and analyze its complexity under various assumptions. Our results show that this is a rich problem to study and shed some light on the difficulty of developing algorithms that preserve utility without compromising privacy.

References

  1. Coral8: www.coral8.com.Google ScholarGoogle Scholar
  2. Streambase: www.streambase.com.Google ScholarGoogle Scholar
  3. Streaminsight: http://www.microsoft.com/sqlserver/2008/en/us/r2-complex-event.aspx.Google ScholarGoogle Scholar
  4. J. Agrawal, Y. Diao, D. Gyllstrom, and N. Immerman. Efficient pattern matching over event streams. In SIGMOD, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. H. Ali and C. G. et al. Microsoft cep server and online behavioral targeting. In VLDB, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. N. Bansal, N. Korula, V. Nagarajan, and A. Srinivasan. On k-column sparse packing programs. CoRR, 0908.2256, 2009.Google ScholarGoogle Scholar
  7. R. S. Barga, J. Goldstein, M. Ali, and M. Hong. Consistent streaming through time: A vision for event stream processing. 2007.Google ScholarGoogle Scholar
  8. U. Feige. A threshold of ln n for approximating set cover. Journal of the ACM, 45 (4), 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Fujishige. Submodular functions and optimization. 2005.Google ScholarGoogle Scholar
  10. D. Gyllstrom, E. Wu, H.-J. Chae, Y. Diao, P. Stahlberg, and G. Anderson. SASE: Complex event processing over streams. In CIDR, 2007.Google ScholarGoogle Scholar
  11. S. Iwata and K. Nagano. Submodular function minimization under covering constraints. In FOCS, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. P. Kall and S. W. Wallace. Stochastic Programming. Wiley, 1994.Google ScholarGoogle Scholar
  13. C. Koufogiannakis and N. E. Young. Greedy Δ-approximation algorithm for covering with arbitrary constraints and submodular cost. In Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I, pages 634--652, Berlin, Heidelberg, 2009. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik. Quantitative System Performance. Prentice Hall, 1984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. H. W. Lenstra. Integer programming with a fixed number of variables. Mathematics of Operation Research, 1983.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. M. Liu, M. Li, D. Golovnya, E. A. Rundensteiner, and K. Claypool. Sequence pattern query processing over out-of-order event streams. In ICDE, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Y. Mei and S. Madden. Zstream: A cost-based query processor for adaptively detecting composite events. In SIGMOD, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. D. Pritchard and D. Chakrabarty. Approximability of sparse integer programs. CoRR, 0904.0859, 2009.Google ScholarGoogle Scholar
  19. L. Trevisan. Inapproximability of combinatorial optimization problems. Technical report, University of California Berkeley, 2004.Google ScholarGoogle Scholar
  20. D. Wang, E. Rundensteiner, and R. Ellison. Active complex event processing for realtime health care. In VLDB, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. W. White, M. Riedewald, J. Gehrke, and A. Demers. What is "next" in event processing? In PODS, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. E. Wu, Y. Diao, and S. Rizvi. High-performance complex event processing over streams. In SIGMOD, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. On the complexity of privacy-preserving complex event processing

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      PODS '11: Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
      June 2011
      332 pages
      ISBN:9781450306607
      DOI:10.1145/1989284

      Copyright © 2011 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 June 2011

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate476of1,835submissions,26%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!