Advertisement

Ask a Better Question, Get a Better Answer A New Approach to Private Data Analysis

  • Cynthia Dwork
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4353)

Abstract

Cryptographic techniques for reasoning about information leakage have recently been brought to bear on the classical problem of statistical disclosure control – revealing accurate statistics about a population while preserving the privacy of individuals. This new perspective has been invaluable in guiding the development of a powerful approach to private data analysis, founded on precise mathematical definitions, and yielding algorithms with provable, meaningful, privacy guarantees.

Keywords

Good Answer Statistical Database Impossibility Result Good Question True Answer 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adam, N.R., Wortmann, J.C.: Security-Control Methods for Statistical Databases: A Comparative Study. ACM Computing Surveys 21(4), 515–556 (1989)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R., Thomas, D.: Privacy Preserving OLAP. In: Proceedings of SIGMOD 2005 (2005)Google Scholar
  3. 3.
    Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical privacy: The SuLQ framework. In: Proceedings of the 24th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 128–138 (2005)Google Scholar
  4. 4.
    Candes, E.J., Rudelson, M., Tao, T., Vershynin, R.: Error Correction via Linear Programming. In: Proceedings of the 46th IEEE Annual Symposium on Foundations of Computer Science (2005)Google Scholar
  5. 5.
    Dalenius, T.: Towards a methodology for statistical disclosure control. Statistik Tidskrift 15, 222–429 (1977)Google Scholar
  6. 6.
    Denning, D.E.: Secure statistical databases with random sample queries. ACM Transactions on Database Systems 5(3), 291–315 (1980)MATHCrossRefGoogle Scholar
  7. 7.
    Dinur, I., Nissim, K.: Revealing information while preserving privacy. In: Proceedings of the 22nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 202–210 (2003)Google Scholar
  8. 8.
    Donoho, D.: For Most Large Underdetermined Systems of Linear Equations, the minimal l1-norm solution is also the sparsest solution (2004) manuscript, Available at: http://stat.stanford.edu/~donoho/reports.html
  9. 9.
    Donoho, D.: For Most Large Underdetermined Systems of Linear Equations, the minimal l1-norm near-solution approximates the sparsest near-solution (2004) manuscript, Available at: http://stat.stanford.edu/~donoho/reports.html
  10. 10.
    Dwork, C.: Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, Springer, Heidelberg (2006) (invited Paper)CrossRefGoogle Scholar
  11. 11.
    Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our Data, Ourselves: Privacy via Distributed Noise Generation. In: Proceedings of Eurocrypt 2006 (2006)Google Scholar
  12. 12.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Proceedings of the 3rd Theory of Cryptography Conference, pp. 265–284 (2006)Google Scholar
  13. 13.
    Dwork, C., Nissim, K.: Privacy-preserving datamining on vertically partitioned databases. In: Franklin, M. (ed.) CRYPTO 2004. LNCS, vol. 3152, pp. 528–544. Springer, Heidelberg (2004)Google Scholar
  14. 14.
    Evfimievski, A., Gehrke, J., Srikant, R.: Limiting privacy breaches in privacy preserving data mining. In: Proceedings of the 22nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 211–222 (June 2003)Google Scholar
  15. 15.
    Goldwasser, S., Micali, S.: Probabilistic encryption. Journal of Computer and System Sciences 28, 270–299 (1984); In: (prelminary version appeared) Proceedings 14th Annual ACM Symposium on Theory of Computing (1982)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cynthia Dwork
    • 1
  1. 1.Microsoft Research 

Personalised recommendations