Abstract
Preserving privacy of an individual in network structured data while enhancing utility of published data is one of the most challenging problems in data privacy. Moreover, different individuals might have different privacy levels based on their own preferences, thereby personalization needs to be considered to achieve personal data protection. In this paper, we aim to develop a privacy-preserving mechanism to publish network statistics, particularly degree distribution, and joint degree distribution, which guarantees personalized (edge or node) differential privacy while enhancing network data utility. To this extend we propose four approaches to handle personal privacy requirements of individuals in a differentially private computation. We have empirically verified the utility enhancement and privacy guarantee of our proposed approaches on four real-world network datasets. To the best of our knowledge, this is the first study to publish network data distributions under personalized differential privacy, while enhancing network data utility.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Network datasets are available at http://snap.stanford.edu/data/index.html .
References
Alaggan, M., Gambs, S., Kermarrec, A.-M.: Heterogeneous differential privacy. J. Priv. Confidentiality 7(2), 127–158 (2016)
Day, W.Y., Li, N., Lyu, M.: Publishing graph degree distribution with node differential privacy. In: SIGMOD, pp. 123–138 (2016)
Ding, X., Zhang, X., Bao, Z., Jin, H.: Privacy-preserving triangle counting in large graphs. In: CIKM, pp. 1283–1292 (2018)
Domingo-Ferrer, J., Torra, V.: Ordinal, continuous and heterogeneous k-anonymity through microaggregation. KDD 11(2), 195–212 (2005)
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). https://doi.org/10.1007/11681878_14
Ebadi, H., Sands, D., Schneider, G.: Differential privacy: now it’s getting personal. POPL 50(1), 69–81 (2015)
Iftikhar, M., Wang, Q.: dK-projection: publishing graph joint degree distribution with node differential privacy. In: Karlapalem, K., et al. (eds.) PAKDD 2021. LNCS (LNAI), vol. 12713, pp. 358–370. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75765-6_29
Iftikhar, M., Wang, Q., Lin, Y.: Publishing differentially private datasets via stable microaggregation. In: EDBT, pp. 662–665 (2019)
Iftikhar, M., Wang, Q., Lin, Yu.: dK-microaggregation: anonymizing graphs with differential privacy guarantees. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 191–203. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47436-2_15
Jorgensen, Z., Yu, T., Cormode, G.: Conservative or liberal? Personalized differential privacy. In: ICDE, pp. 1023–1034. IEEE (2015)
Jorgensen, Z., Yu, T., Cormode, G.: Publishing attributed social graphs with formal privacy guarantees. In: SIGMOD, pp. 107–122 (2016)
Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately? SICOMP 40(3), 793–826 (2011)
Koufogiannis, F., Han, S., Pappas, G.J.: Gradual release of sensitive data under differential privacy. J. Priv. Confidentiality 7(2), 23–52 (2016)
Koufogiannis, F., Pappas, G.J.: Diffusing private data over networks. TCNS 5(3), 1027–1037 (2017)
Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW, pp. 591–600 (2010)
Li, H., Xiong, L., Ji, Z., Jiang, X.: Partitioning-Based Mechanisms Under Personalized Differential Privacy. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 615–627. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_48
Li, N., Qardaji, W., Su, D.: On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy. In: ASIACCS, pp. 32–33 (2012)
Lin, B.R., Wang, Y., Rane, S.: On the benefits of sampling in privacy preserving statistical analysis on distributed databases. CoRR, abs/1304.4613 (2013)
Mahadevan, P., Hubble, C., Krioukov, D., Huffaker, B., Vahdat, A.: Orbis: rescaling degree correlations to generate annotated internet topologies. In: SIGCOMM, vol. 37, pp. 325–336 (2007)
Mahadevan, P., Krioukov, D., Fall, K., Vahdat, A.: Systematic topology analysis and generation using degree correlations. In: SIGCOMM, pp. 135–146 (2006)
McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: SIGMOD, pp. 19–30 (2009)
Sala, A., Zhao, X., Wilson, C., Zheng, H., Zhao, B.Y.: Sharing graphs using differentially private graph models. In: SIGCOMM, pp. 81–98 (2011)
Wang, Y., Wu, X.: Preserving differential privacy in degree-correlation based graph generation. TDP 6(2), 127–145 (2013)
Yan, S., Pan, S., Zhao, Y., Zhu, W.-T.: Towards privacy-preserving data mining in online social networks: distance-grained and item-grained differential privacy. In: Liu, J.K., Steinfeld, R. (eds.) ACISP 2016. LNCS, vol. 9722, pp. 141–157. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40253-6_9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Iftikhar, M., Wang, Q., Li, Y. (2022). dK-Personalization: Publishing Network Statistics with Personalized Differential Privacy. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-05933-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05932-2
Online ISBN: 978-3-031-05933-9
eBook Packages: Computer ScienceComputer Science (R0)