Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams

  • T. -H. Hubert Chan
  • Mingfei Li
  • Elaine Shi
  • Wenchang Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7384)


We consider applications scenarios where an untrusted aggregator wishes to continually monitor the heavy-hitters across a set of distributed streams. Since each stream can contain sensitive data, such as the purchase history of customers, we wish to guarantee the privacy of each stream, while allowing the untrusted aggregator to accurately detect the heavy hitters and their approximate frequencies. Our protocols are scalable in settings where the volume of streaming data is large, since we guarantee low memory usage and processing overhead by each data source, and low communication overhead between the data sources and the aggregator.


Failure Probability Communication Cost Bloom Filter Current Window Differential Privacy 
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.


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  1. 1.
    Arasu, A., Manku, G.S.: Approximate counts and quantiles over sliding windows. In: PODS (2004)Google Scholar
  2. 2.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)CrossRefzbMATHGoogle Scholar
  3. 3.
    Chan, H.-L., Lam, T.-W., Lee, L.-K., Ting, H.-F.: Continuous monitoring of distributed data streams over a time-based sliding window. In: STACS (2010)Google Scholar
  4. 4.
    Chan, T.-H.H., Li, M., Shi, E., Xu, W.: Differentially private continual monitoring of heavy hitters from distributed streams. In: Cryptology ePrint Archive (2012)Google Scholar
  5. 5.
    Hubert Chan, T.-H., Shi, E., Song, D.: Private and Continual Release of Statistics. In: Abramsky, S., Gavoille, C., Kirchner, C., Meyer auf der Heide, F., Spirakis, P.G. (eds.) ICALP 2010, Part II. LNCS, vol. 6199, pp. 405–417. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Demaine, E.D., López-Ortiz, A., Munro, J.I.J.: Frequency Estimation of Internet Packet Streams with Limited Space. In: Möhring, R.H., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, pp. 348–360. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Dwork, C.: Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006, Part II. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)CrossRefGoogle Scholar
  9. 9.
    Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N.: Differential privacy under continual observation. In: STOC, pp. 715–724 (2010)Google Scholar
  10. 10.
    Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N., Yekhanin, S.: Pan-private streaming algorithms. In: ICS, pp. 66–80 (2010)Google Scholar
  11. 11.
    Ganta, S.R., Kasiviswanathan, S.P., Smith, A.: Composition attacks and auxiliary information in data privacy. In: KDD, pp. 265–273 (2008)Google Scholar
  12. 12.
    Ghosh, A., Roughgarden, T., Sundararajan, M.: Universally utility-maximizing privacy mechanisms. In: STOC (2009)Google Scholar
  13. 13.
    Goldreich, O.: The Foundations of Cryptography - Volume 2, Basic Applications. Cambridge University Press (2004)Google Scholar
  14. 14.
    Karp, R.M., Shenker, S., Papadimitriou, C.H.: A simple algorithm for finding frequent elements in streams and bags. ACM Trans. Database Syst. 28, 51–55 (2003)CrossRefGoogle Scholar
  15. 15.
    Kursawe, K., Danezis, G., Kohlweiss, M.: Privacy-Friendly Aggregation for the Smart-Grid. In: Fischer-Hübner, S., Hopper, N. (eds.) PETS 2011. LNCS, vol. 6794, pp. 175–191. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Lee, L.K., Ting, H.F.: A simpler and more efficient deterministic scheme for finding frequent items over sliding windows. In: PODS (2006)Google Scholar
  17. 17.
    McSherry, F.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: SIGMOD Conference, pp. 19–30 (2009)Google Scholar
  18. 18.
    Mir, D.J., Muthukrishnan, S., Nikolov, A., Wright, R.N.: Pan-private algorithms via statistics on sketches. In: PODS, pp. 37–48 (2011)Google Scholar
  19. 19.
    Misra, J., Gries, D.: Finding repeated elements. Sci. Comput. Program. 2(2), 143–152 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Rastogi, V., Nath, S.: Differentially private aggregation of distributed time-series with transformation and encryption. In: SIGMOD 2010, pp. 735–746 (2010)Google Scholar
  21. 21.
    Shi, E., Chan, H., Rieffel, E., Chow, R., Song, D.: Privacy-preserving aggregation of time-series data. In: NDSS (2011)Google Scholar
  22. 22.
    Yi, K., Zhang, Q.: Optimal tracking of distributed heavy hitters and quantiles. In: PODS (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • T. -H. Hubert Chan
    • 1
  • Mingfei Li
    • 1
  • Elaine Shi
    • 2
  • Wenchang Xu
    • 3
  1. 1.The University of Hong KongHong Kong
  2. 2.UC BerkeleyUSA
  3. 3.Tsinghua UniversityChina

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