Skip to main content

Some Additional Insights on Applying Differential Privacy for Numeric Data

  • Conference paper
Privacy in Statistical Databases (PSD 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6344))

Included in the following conference series:

Abstract

Recently Sarathy and Muralidhar (2009) provided the first attempt at illustrating the implementation of differential privacy for numerical data. In this paper, we attempt to provide further insights on the results that are observed when Laplace based noise addition is used to protect numerical data in order to satisfy differential privacy. Our results raise serious concerns regarding the viability of differential privacy and Laplace noise addition as appropriate procedures for protecting numerical data.

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

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.

References

  1. Abowd, J.M., Vilhuber, L.: How Protective Are Synthetic Data? In: Domingo-Ferrer, J., Saygın, Y. (eds.) PSD 2008. LNCS, vol. 5262, pp. 239–246. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  2. Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. In: Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS), pp. 289–296 (2008)

    Google Scholar 

  3. Denning, D.E., Denning, P.J., Schwartz, M.D.: The tracker: A threat to statistical database security. ACM Transactions on Database Systems 4, 76–96 (1979)

    Article  Google Scholar 

  4. Dinur, I., Nissim, K.: Revealing Information while Preserving Privacy. In: PODS 2003, San Diego, CA, pp. 202–210 (2003)

    Google Scholar 

  5. Duncan, G.T., Mukherjee, S.: Optimal Disclosure Limitation Strategy in Statistical Databases: Deterring Tracker Attacks Through Additive Noise. Journal of the American Statistical Association 95, 720–729 (2000)

    Article  Google Scholar 

  6. Dwork, C., Smith, A.: Differential Privacy for Statistics: What we Know and What we Want to Learn. Journal of Privacy and Confidentiality 1, 135–154 (2009)

    Google Scholar 

  7. 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)

    Chapter  Google Scholar 

  8. Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., Vilhuber, L.: Privacy: From Theory to Practice on the Map. In: ICDE, pp. 277–286. IEEE Computer Society, Los Alamitos (2008)

    Google Scholar 

  9. Nissim, K., Raskhodnokova, S., Smith, A.: Smooth Sensitivity and Sampling in Private Data Analysis. In: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pp. 75–84 (2007)

    Google Scholar 

  10. Sarathy, R., Muralidhar, K.: Differential Privacy for Numeric Data. In: Joint UNECE/Eurostat work session on statistical data confidentiality, Bilbao, Spain (2009)

    Google Scholar 

  11. Wasserman, L., Zhou, S.: A Statistical Framework for Differential Privacy. Journal of the American Statistical Association 105, 375–389 (2009)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sarathy, R., Muralidhar, K. (2010). Some Additional Insights on Applying Differential Privacy for Numeric Data. In: Domingo-Ferrer, J., Magkos, E. (eds) Privacy in Statistical Databases. PSD 2010. Lecture Notes in Computer Science, vol 6344. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15838-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15838-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15837-7

  • Online ISBN: 978-3-642-15838-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics