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Private and Continual Release of Statistics

Published:01 November 2011Publication History
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Abstract

We ask the question: how can Web sites and data aggregators continually release updated statistics, and meanwhile preserve each individual user’s privacy? Suppose we are 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 Web sites to continually give top-k and hot items suggestions while preserving users’ privacy.

References

  1. Calandrino, J. A., Kilzer, A, Narayanan, A., Felten, E. W., and Shmatikov, V. 2011. “You might also like:” Privacy risks of collaborative filtering. In Proceedings of the IEEE Symposium on Security and Privacy. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Demaine, E. D., López-Ortiz, A., and Munro, J. I. 2002. Frequency estimation of internet packet streams with limited space. In Proceedings of the 10th Annual European Symposium on Algorithms (ESA’02). Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Dinur, I. and Nissim, K. 2003. Revealing information while preserving privacy. In Proceedings of the ACM SIGACT-SIGMOND-SIGART Symposium on Principles of Database Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Dwork, C. 2006. Differential privacy. In Proceedings of the 33rd International Colloquium on Automata, Languages and Programming. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Dwork, C. 2008. Differential privacy: A survey of results. In Proceedings of the 5th Annual Conference on Theory and Applications of Models of Computation. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Dwork, C. 2009. The differential privacy frontier. In Proceedings of the Theory of Computing Conference.Google ScholarGoogle Scholar
  7. Dwork, C. 2010a. Differential privacy in new settings. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA). Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Dwork, C. 2010b. A firm foundation for private data analysis. Comm. ACM 54, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Dwork, C. and Yekhanin, S. 2008. New efficient attacks on statistical disclosure control mechanisms. In Proceedings of the CRYPTO’08. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Dwork, C., McSherry, F., Nissim, K., and Smith, A. 2006. Calibrating noise to sensitivity in private data analysis. In Proceedings of the 3rd IACR Theory of Cryptography Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Dwork, C., Naor, M., Pitassi, T., and Rothblum, G. N. 2010a. Differential privacy under continual observation. In Proceedings of the Annual ACM Symposium on Theory of Computing. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Dwork, C., Naor, M., Pitassi, T., Rothblum, G. N., and Yekhanin, S. 2010b. Pan-private streaming algorithms. In Proceedings of the Conference on Innovations in Computer Science.Google ScholarGoogle Scholar
  13. Hay, M., Rastogi, V., Miklau, G., and Suciu, D. 2010. Boosting the accuracy of differentially private histograms through consistency. Proc. VLDB 3, 1, 1021--1032. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Jones, R., Kumar, R., Pang, B., and Tomkins, A. 2008. Vanity fair: Privacy in querylog bundles. In Proceedings of the International Conference on Information and Knowledge Management. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Korolova, A., Kenthapadi, K., Mishra, N., and Ntoulas, A. 2009. Releasing search queries and clicks privately. In Proceedings of the International World Wide Web Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Li, C., Hay, M., Rastogi, V., Miklau, G., and McGregor, A. 2010. Optimizing linear counting queries under differential privacy. In Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. 123--134. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Manku, G. S. and Motwani, R. 2002. Approximate frequency counts over data streams. In Proceedings of the International Conference on Very Large Databases. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. McSherry, F. and Mironov, I. 2009. Differentially private recommender systems: Building privacy into the netflix prize contenders. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Metwally, A., Agrawal, D., and Abbadi, A. E. 2005. Efficient computation of frequent and top-k elements in data streams. In Proceedings of the International Conference on Database Theory. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Narayanan, A. and Shmatikov, V. 2008. Robust de-anonymization of large sparse datasets. In Proceedings of the IEEE Symposium on Security and Privacy. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Warner, S. L. 1965. Randomized response: A survey technique for eliminating evasive answer bias. J. Amer. Stat. Assn.Google ScholarGoogle ScholarCross RefCross Ref
  22. Xiao, X., Wang, G., and Gehrke, J. 2010. Differential privacy via wavelet transforms. In Proceedings of the International Conference on Data Engineering. 225--236.Google ScholarGoogle Scholar
  23. Yeganova, L. and Wilbur, W. 2009. Isotonic regression under Lipschitz constraint. J. Optimiz. Theory Appl 141, 429--443.Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Transactions on Information and System Security
      ACM Transactions on Information and System Security  Volume 14, Issue 3
      November 2011
      133 pages
      ISSN:1094-9224
      EISSN:1557-7406
      DOI:10.1145/2043621
      Issue’s Table of Contents

      Copyright © 2011 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 November 2011
      • Accepted: 1 June 2011
      • Revised: 1 January 2011
      • Received: 1 May 2010
      Published in tissec Volume 14, Issue 3

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