Skip to main content

Differential Privacy

  • Conference paper
Automata, Languages and Programming (ICALP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4052))

Included in the following conference series:

Abstract

In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius’ goal along the lines of semantic security cannot be achieved. Contrary to intuition, a variant of the result threatens the privacy even of someone not in the database. This state of affairs suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one’s privacy incurred by participating in a database. The techniques developed in a sequence of papers [8, 13, 3], culminating in those described in [12], can achieve any desired level of privacy under this measure. In many cases, extremely accurate information about the database can be provided while simultaneously ensuring very high levels of privacy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Adam, N.R., Wortmann, J.C.: Security-Control Methods for Statistical Databases: A Comparative Study. ACM Computing Surveys 21(4), 515–556 (1989)

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proc, A.S. (ed.) International Conference on Management of Data, pp. 439–450 (2000)

    Google Scholar 

  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, June 2005, pp. 128–138 (2005)

    Google Scholar 

  4. Chawla, S., Dwork, C., McSherry, F., Smith, A., Wee, H.: Toward privacy in public databases. In: Proceedings of the 2nd Theory of Cryptography Conference, pp. 363–385 (2005)

    Google Scholar 

  5. Chawla, S., Dwork, C., McSherry, F., Talwar, K.: On the utility of privacy-preserving histograms. In: Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (2005)

    Google Scholar 

  6. Dalenius, T.: Towards a methodology for statistical disclosure control. Statistik Tidskrift 15, 222–429 (1977)

    Google Scholar 

  7. Denning, D.E.: Secure statistical databases with random sample queries. ACM Transactions on Database Systems 5(3), 291–315 (1980)

    Article  MATH  Google Scholar 

  8. 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 

  9. Dobkin, D., Jones, A.K., Lipton, R.J.: Secure databases: Protection against user influence. ACM Trans. Database Syst. 4 1, 97–106 (1979)

    Article  Google Scholar 

  10. Dodis, Y., Reyzin, L., Smith, A.: Fuzzy extractors: How to generate strong keys from biometrics and other noisy data. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 523–540. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  11. Dodis, Y., Smith, A.: Correcting Errors Without Leaking Partial Information. In: Proceedings of the 37th ACM Symposium on Theory of Computing, pp. 654–663 (2005)

    Google Scholar 

  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. Dwork, C., Nissim, K.: Privacy-preserving datamining on vertically partitioned databases. In: Advances in Cryptology: Proceedings of Crypto, pp. 528–544 (2004)

    Google Scholar 

  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, June 2003, pp. 211–222 (2003)

    Google Scholar 

  15. Goldwasser, S., Micali, S.: Probabilistic encryption. Journal of Computer and System Sciences 28, 270–299 (1984); prelminary version appeared in Proceedings 14th Annual ACM Symposium on Theory of Computing

    Article  MATH  MathSciNet  Google Scholar 

  16. Nisan, N., Zuckerman, D.: Randomness is linear in space. J. Comput. Syst. Sci. 52(1), 43–52 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  17. Shaltiel, R.: Recent developments in explicit constructions of extractors. Bulletin of the EATCS 77, 67–95 (2002)

    MATH  MathSciNet  Google Scholar 

  18. Sweeney, L.: Weaving technology and policy together to maintain confidentiality. J. of Law Med Ethics 25(2-3), 98–110 (1997)

    Article  Google Scholar 

  19. Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10(5), 571–588 (2002)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dwork, C. (2006). Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds) Automata, Languages and Programming. ICALP 2006. Lecture Notes in Computer Science, vol 4052. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11787006_1

Download citation

  • DOI: https://doi.org/10.1007/11787006_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35907-4

  • Online ISBN: 978-3-540-35908-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics