Abstract
The eruption of social networks, communication networks etc. makes them become valuable resources for the research community. However, the graph data owners hesitate to share their data due to the barrier of privacy leakage. In this work, we propose a new privacy definition, called subgraph-differential privacy (subgraph-DP), for graph data publishing based on the conventional differential privacy definition. Subgraph-DP is against the subgraph-based attacks by restricting the adversaries predict the true subgraph with a high confidence. We provide the mechanism that gives subgraph-DP in which noise will be added to a small set of edges to make sure that all k -vertices connected subgraphs are perturbed. The experimental results show that our perturbation mechanism preserves most of the important statistic features of graph while still guarantees privacy.
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
References
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)
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)
Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: ICDE (2008)
Ahmed, F., Jin, R., Liu, A.X.: A random matrix approach to differential privacy and structure preserved social network graph publishing. ArXiv preprint (2013). arXiv:1307.0475
Sala, A., Zhao, X., Wilson, C., Zheng, H., Zhao, B.Y.: Sharing graphs using differentially private graph models. In: IMC, pp. 81–98 (2011)
Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: S&P, pp. 111–125 (2008)
Nilizadeh, S., Kapadia, A., Ahn, Y.Y.: Community-enhanced De-anonymization of online social networks. In: CCS (2014)
Cheng, J., Fu, A.W.C., Fu, J.: K-Isomorphism: privacy preserving network publication against structural attacks. In: SIGMOD (2010)
Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: KDD, pp. 397–406, Paris, France (2009)
Alvisi, L., Clement, A., Epasto, A., Lattanzi, S., Panconesi, A.: The evolution of sybil defense via social networks. In: S&P (2013)
Harel, D., Koren, Y.: On clustering using random walks. In: Hariharan, R., Mukund, M., Vinay, V. (eds.) FSTTCS 2001. LNCS, vol. 2245, pp. 18–41. Springer, Heidelberg (2001)
Kasiviswanathan, S.P., Nissim, K., Raskhodnikova, S., Smith, A.: Analyzing graphs with node differential privacy. In: Sahai, A. (ed.) TCC 2013. LNCS, vol. 7785, pp. 457–476. Springer, Heidelberg (2013)
McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: SIGMOD, pp. 19–30 (2009)
Le Ny, J., Pappas, G.J.: Differentially private filtering. In: CDC, pp. 3398–3403 (2012)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley-interscience, Hoboken (2006)
Hay, M., Rastogi, V., Miklau, G., Suciu, D.: Boosting the accuracy of differentiallyprivate histograms through consistency. Proc. VLDB Endow. 3(1–2), 1021–1032 (2010)
Chen, S., Zhou, S., Bhowmick, S.S.: Integrating historical noisy answers for improving data utility under differential privacy. In: EDBT, pp. 62–73 (2012)
Acknowledgments
We are grateful to anonymous reviewers who give us helpful advices, support us improve our work. This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. H0510-14-1004, Subgraph differential privacy for graph data publishing)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Nguyen, B.P., Ngo, H., Kim, J., Kim, J. (2016). Publishing Graph Data with Subgraph Differential Privacy. In: Kim, Hw., Choi, D. (eds) Information Security Applications. WISA 2015. Lecture Notes in Computer Science(), vol 9503. Springer, Cham. https://doi.org/10.1007/978-3-319-31875-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-31875-2_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31874-5
Online ISBN: 978-3-319-31875-2
eBook Packages: Computer ScienceComputer Science (R0)