Abstract
Excessive sensitivity problem due to complication of data has been a non-negligible challenge to data privacy protection under differential privacy recently. We design a private data release framework called DPDR-SKG (Differentially Private Data Release via Stochastic Kronecker Graph), which focuses on releasing social network data under differential privacy and uses a two-phase privacy budget allocation. Firstly, we cluster the similar communities of network according to Stochastic Kronecker graph parameter. Secondly, we implement optimized privacy budget allocation in terms of cluster distribution. Experimental results show that the DPDR-SKG outperforms in preserving the privacy of network structure and effectively retaining the data utility.
W. Zhang—Project supported by the National Natural Science Foundation of China under grants 61272422, 61202353.
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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)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 571–588 (2002)
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)
Zhu, T., Xiong, P., Li, G., Zhou, W.: Correlated differential privacy: hiding information in non-IID data set. IEEE Trans. Inf. Forensics Secur. 10(2), 229–242 (2015)
Karwa, V., Slavković, A.B.: Differentially private graphical degree sequences and synthetic graphs. In: Domingo-Ferrer, J., Tinnirello, I. (eds.) PSD 2012. LNCS, vol. 7556, pp. 273–285. Springer, Heidelberg (2012)
Sala, A., Zhao, X., Wilson, C., Zheng, H., Zhao, B.Y.: Sharing graphs using differentially private graph models. In: Proceedings of 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 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 911–920. ACM (2014)
Lu, W., Miklau, G.: Exponential random graph estimation under differential privacy. In: Proceedings of 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 921–930. ACM (2014)
Mir, D.J., Wright, R.N.: A differentially private graph estimator. In: IEEE International Conference on Data Mining Workshops, ICDMW 2009, pp. 122–129. IEEE (2009)
Gleich, D.F., Owen, A.B.: Moment-based estimation of stochastic Kronecker graph parameters. Internet Math. 8(3), 232–256 (2012)
Hay, M., Li, C., Miklau, G., Jensen, D.: Accurate estimation of the degree distribution of private networks. In: 9th IEEE International Conference on Data Mining, ICDM 2009, pp. 169–178. IEEE (2009)
Rossi, N.A.R.: Network repository (2012–2016). http://networkrepository.com/index.php
DuBois, C.L.: UCI network data repository (2008)
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Li, D., Zhang, W., Chen, Y. (2016). Differentially Private Network Data Release via Stochastic Kronecker Graph. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_23
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DOI: https://doi.org/10.1007/978-3-319-48743-4_23
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