Private and Continual Release of Statistics

  • T-H. Hubert Chan
  • Elaine Shi
  • Dawn Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6199)


We ask the question – how can websites and data aggregators continually release updated statistics, and meanwhile preserve each individual user’s privacy? Given a stream of 0’s and 1’s, we propose a differentially private continual counter that outputs at every time step the approximate number of 1’s seen thus far. Our counter construction has error that is only poly-log in the number of time steps. We can extend the basic counter construction to allow websites to continually give top-k and hot items suggestions while preserving users’ privacy.


Full Version Laplace Distribution Differential Privacy Traditional Setting True Count 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Calandrino, J.A., Narayanan, A., Felten, E.W., Shmatikov, V.: Don’t review that book: Privacy risks of collaborative filtering. Manuscript (2009)Google Scholar
  2. 2.
    Demaine, E.D., López-Ortiz, A., Munro, J.I.: Frequency estimation of internet packet streams with limited space. In: Möhring, R.H., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, p. 348. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Dinur, I., Nissim, K.: Revealing information while preserving privacy. In: PODS (2003)Google Scholar
  4. 4.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Dwork, C.: The differential privacy frontier. In: Reingold, O. (ed.) TCC 2009. LNCS, vol. 5444, pp. 496–502. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Dwork, C.: Differential privacy in new settings. In: Invited presentation at ACM-SIAM Symposium on Discrete Algorithms, SODA (2010)Google Scholar
  7. 7.
    Dwork, C.: A firm foundation for private data analysis. Communications of the ACM (2010)Google Scholar
  8. 8.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N.: Differential privacy under continual observation. In: STOC (2010)Google Scholar
  10. 10.
    Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N., Yekhanin, S.: Pan-private streaming algorithms. In: Innovations in Computer Science, ISC (2010)Google Scholar
  11. 11.
    Dwork, C., Yekhanin, S.: New efficient attacks on statistical disclosure control mechanisms. In: Wagner, D. (ed.) CRYPTO 2008. LNCS, vol. 5157, pp. 469–480. Springer, Heidelberg (2008)Google Scholar
  12. 12.
    Jones, R., Kumar, R., Pang, B., Tomkins, A.: Vanity fair: privacy in querylog bundles. In: CIKM (2008)Google Scholar
  13. 13.
    Korolova, A., Kenthapadi, K., Mishra, N., Ntoulas, A.: Releasing search queries and clicks privately. In: WWW (2009)Google Scholar
  14. 14.
    Manku, G.S., Motwani, R.: Approximate frequency counts over data streams. In: VLDB (2002)Google Scholar
  15. 15.
    McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the netflix prize contenders. In: KDD (2009)Google Scholar
  16. 16.
    Metwally, A., Agrawal, D., Abbadi, A.E.: Efficient computation of frequent and top-k elements in data streams. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 398–412. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy (2008)Google Scholar
  18. 18.
    Song, D., Hubert Chan, T.-H., Shi, E. Private and continual release of statistics (2010),
  19. 19.
    Warner, S.L.: Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association (1965)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • T-H. Hubert Chan
    • 1
  • Elaine Shi
    • 2
  • Dawn Song
    • 3
  1. 1.The University of Hong Kong 
  2. 2.PARC 
  3. 3.UC Berkeley 

Personalised recommendations