Abstract
Following the trend of Online Social Networks (OSNs) data sharing and publishing, researchers raise their concern about the privacy problem. Differential privacy is such a mechanism to anonymize sensitive data. It deploys graph abstraction models, such as the Hierarchical Random Graph (HRG) model, to extract graph features. However, the injected noise amount, determined by the sensitivity, is usually proportion to the size of the whole network. Therefore, achieving global differential privacy may harm the utility of the releasing graphs.
In this paper, we define the notion of group-based local differential privacy. In particular, by splitting the network into 1-neighborhood graphs and applying HRG-based methods, our scheme reduces the noise scale on local graphs when achieving differential privacy. By deploying the grouping algorithm, our scheme focuses on anonymizing similar users. The experiment results show that our scheme could preserve more utility than the global scheme under the same privacy level.
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References
Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 173–187. IEEE (2009)
Ji, S., Li, W., Mittal, P., Hu, X., Beyah, R.: SecGraph: a uniform and open-source evaluation system for graph data anonymization and de-anonymization. In: Proceedings of USENIX Security Symposium (2015)
Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 506–515. IEEE (2008)
Zou, L., Chen, L., Tamer Özsu, M.: K-automorphism: a general framework for privacy preserving network publication. Proc. VLDB Endowment 2(1), 946–957 (2009)
Dwork, C.: Differential privacy. In: van Tilborg, H.C.A., Jajodia, S. (eds.) Encyclopedia of Cryptography and Security, pp. 338–340. Springer, New York (2011)
Chen, R., Acs, G., Castelluccia, C.: Differentially private sequential data publication via variable-length n-grams. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 638–649. ACM (2012)
Sala, A., Zhao, X., Wilson, C., Zheng, H., Zhao, B.Y.: Sharing graphs using differentially private graph models. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 81–98. ACM (2011)
Xiao, Q., Chen, R., Tan, K.-L.: Differentially private network data release via structural inference. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 911–920. ACM (2014)
Boppana, R., Halldórsson, M.M.: Approximating maximum independent sets by excluding subgraphs. BIT Numer. Math. 32(2), 180–196 (1992)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM (2010)
Leskovec, J., Krevl, A.: SNAP datasets: Stanford large network dataset collection, June 2014
Clauset, A., Moore, C., Newman, M.E.J.: Structural inference of hierarchies in networks. In: Airoldi, E., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) ICML 2006. LNCS, vol. 4503, pp. 1–13. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73133-7_1
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Gao, T., Li, F., Chen, Y., Zou, X. (2017). Preserving Local Differential Privacy in Online Social Networks. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_35
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DOI: https://doi.org/10.1007/978-3-319-60033-8_35
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