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Abstract

Anonymous communication networks like Tor are vulnerable to attackers that control entry and exit nodes. Such attackers can compromise the essential anonymity and privacy properties of the network. In this paper, we consider the path bias attack– where the attacker induces a client to use compromised nodes and thus links the client to their destination. We describe an efficient scheme that detects such attacks in Tor by collecting routing telemetry data from nodes in the network. The data collection is differentially private and thus does not reveal behaviour of individual users even to nodes within the network. We show provable bounds for the sample complexity of the scheme and describe methods to make it resilient to introduction of false data by the attacker to subvert the detection process. Simulations based on real configurations of the Tor network show that the method works accurately in practice.

] Xiaotong Wu, Taotao Wu, Maqbool Khan, Qiang Ni, and Wanchun Dou. Game theory based correlated privacy preserving analysis in big data. IEEE Transactions on Big Data , 2017.

for Remote Access Systems. Statistical Science, 20, 163-177. DOI : http://dx.doi.org/10.1214/088342305000000043 Herzog, T.N., Scheuren, F.L., and Winkler, W.E. (2007). Data Quality and Record Linkage. Berlin: Springer. Hosmer, D.W. and Lemeshow, S. (2000). Applied Logistic Regression. Hoboken, NJ: John Wiley and Sons Inc. Karr, A.F., Lin, X., Sanil, A.P., and Reiter, J.P. (2009). Privacy Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products. Journal of Official Statistics, 25, 125-138. Kohnen, C. and Reiter, J.P. (2009). Multiple Imputation

Association , 105(489):375–389, 2010. [61] Rand R Wilcox. Introduction to robust estimation and hypothesis testing . Academic press, 2011. [62] Xiaotong Wu, Taotao Wu, Maqbool Khan, Qiang Ni, and Wanchun Dou. Game theory based correlated privacy preserving analysis in big data. IEEE Transactions on Big Data , 2017. [63] Yonghui Xiao and Li Xiong. Protecting locations with differential privacy under temporal correlations. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security , pages 1298–1309. ACM, 2015. [64] Bin Yang, Issei Sato, and

. Game theory based privacy preserving analysis in correlated data publication. In Proceedings of the Australasian Computer Science Week Multiconference . ACM, 2017. [170] Xiaotong Wu, Taotao Wu, Maqbool Khan, Qiang Ni, and Wanchun Dou. Game theory based correlated privacy preserving analysis in big data. IEEE Transactions on Big Data , 2017. [171] Yonghui Xiao and Li Xiong. Protecting locations with differential privacy under temporal correlations. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . ACM, 2015. [172] Ziqi Yan