Abstract
Differential privacy (DP) has attracted considerable attention as the method of choice for releasing aggregate query results making it hard to infer information about individual records in the database. The most common way to achieve DP is to add noise following Laplace distribution. In this paper, we study differential privacy from a utility point of view for single and multiple queries. We examine the relationship between the cumulative probability of noise and the privacy degree. Using this analysis and the notion of relative error, we show when for a given problem it is reasonable to employ a differentially private algorithm without losing a certain level of utility. For the case of multiple queries, we introduce a simple DP method called Differential (DIFF) that adds noise proportional to a query index used to express our preferences for having different noise scales for different queries. We also introduce an equation capturing when DIFF satisfies a user-given relative error threshold.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: Privacy, accuracy, and consistency too: a holistic solution to contingency table release. In: PODS, pp. 273–282 (2007)
Ding, B., Winslett, M., Han, J., Li, Z.: Differentially private data cubes: optimizing noise sources and consistency. In: SIGMOD Conference, pp. 217–228 (2011)
Dwork, C.: Differential Privacy: A Survey of Results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008)
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our Data, Ourselves: Privacy Via Distributed Noise Generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006)
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)
Garofalakis, M.N., Kumar, A.: Wavelet synopses for general error metrics. ACM Trans. Database Syst. 30(4), 888–928 (2005)
Hay, M., Rastogi, V., Miklau, G., Suciu, D.: Boosting the accuracy of differentially private histograms through consistency. PVLDB 3(1), 1021–1032 (2010)
Li, C., Hay, M., Rastogi, V., Miklau, G., McGregor, A.: Optimizing linear counting queries under differential privacy. In: PODS, pp. 123–134 (2010)
McSherry, F.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: SIGMOD Conference, pp. 19–30 (2009)
Vitter, J.S., Wang, M.: Approximate computation of multidimensional aggregates of sparse data using wavelets. In: SIGMOD Conference, pp. 193–204 (1999)
Xiao, X., Bender, G., Hay, M., Gehrke, J.: ireduct: differential privacy with reduced relative errors. In: SIGMOD Conference, pp. 229–240 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shoaran, M., Thomo, A., Weber, J. (2012). Differential Privacy in Practice. In: Jonker, W., Petković, M. (eds) Secure Data Management. SDM 2012. Lecture Notes in Computer Science, vol 7482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32873-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-32873-2_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32872-5
Online ISBN: 978-3-642-32873-2
eBook Packages: Computer ScienceComputer Science (R0)