Abstract
The emerging trends in cloud computing have facilitated the integration of existing technologies towards achieving new and innovative applications for the betterment of humans. Remote health monitoring, a bi-product of technology integration, assists in minimizing human mortality through continuous health monitoring using low-cost sensors. However, privacy and security concerns have become a bottleneck in this process. The secure multi-party computation (SMC)-based privacy-preserving data mining algorithm has emerged as a solution to this problem. However, traditional cryptography-based PPDM solutions are too inefficient and infeasible for analysis on large-scale datasets for data owners. Previous work on random decision trees (RDTs) shows that it is possible to generate equivalent and accurate models at substantially lower costs. In this paper, we focus on the outsourced privacy-preserving random decision tree (OPPRDT) algorithm for multiple parties. We outsource most of the protocol computation to the cloud and propose secure sub-protocols to protect users’ data privacy. As a result, we show that our method can achieve similar results as the original RDT algorithm while also preserving the privacy of the data. We prove that there is a sub-linear relationship between the computational cost of the user side and the number of participating parties.
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Yao, A.: How to generate and exchange secrets. In: Proceedings of Annual Symposium on Foundations of Computer Science, pp. 162–167 (1986)
Bresson, E., Catalano, D., Pointcheval, D.: A simple public-key cryptosystem with a double trapdoor decryption mechanism and its applications. In: Laih, C.-S. (ed.) ASIACRYPT 2003. LNCS, vol. 2894, pp. 37–54. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-40061-5_3
Liu, D., Bertino, E., Yi, X.: Privacy of outsourced k-means clustering. In: Proceedings of ACM Symposium on Information, Computer and Communications Security, pp. 123–134 (2014)
Elgamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. In: CRYPTO 1984, Proceedings of Advances in Cryptology, pp. 10–18 (1985)
Fan, W., Wang, H., Yu, P.S., et al.: Is random model better? On its accuracy and efficiency. In: IEEE International Conference on Data Mining, pp. 51–58. DBLP (2003)
Emekci, F., Sahin, O.D., et al.: Privacy preserving decision tree learning over multiple parties. Data Knowl. Eng. 63(2), 348–361 (2007)
Jagannathan, G., Wright, R.N.: Privacy-preserving distributed K-means clustering over arbitrarily partitioned data. In: Proceedings of ACM International Conference on Knowledge Discovery, pp. 593–599 (2005)
Gangrade, A., Patel, R.: Building privacy-preserving C4.5 decision tree classifier on multi-parties. Int. J. Comput. Sci. Eng. 1(3), 199–205 (2009)
Hohenberger, S., Lysyanskaya, A.: How to securely outsource cryptographic computations. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 264–282. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30576-7_15
Zhan, J., Matwin, S., et al.: Privacy preserving decision tree classiffcation over horizontally partitioned data. In: Proceedings of International Conference on Electronic Business, pp. 470–476 (2005)
Zhan, J., Matwin, S., Chang, L.W.: Privacy-preserving decision tree classification over vertically partitioned data. In: Proceedings of IEEE International Conference on Data Mining Workshop on Multiagent Data Warehousing (MADW) and Multiagent Data Mining (MADM), pp. 27–35 (2005)
Kamara, S., Mohassel, P., Raykova, M.: Outsourcing multi-party computation. In: IACR Cryptology Eprint Archive, vol. 2011(3), pp. 435–451 (2011)
Lindell, Y., Pinkas, B.: Pinkas.: privacy preserving data mining. J. Cryptol. 15(3), 177–206 (2002)
Liu, X., Jiang, Z.L., Yiu, S.M., Wang, X.: Outsourcing two-party privacy preserving k-means clustering protocol in wireless sensor networks. In: Proceedings of International Conference on Mobile Ad-Hoc and Sensor Networks, pp. 124–133 (2015)
Liu, X., Deng, R., Choo, K.K.R., et al.: An efficient privacy-preserving outsourced calculation toolkits with multiple keys. IEEE Trans. Inf. Forensics Secur. 11(11), 1–1 (2016)
Xiao, M., Huang, L., et al.: Privacy preserving ID3 algorithm over horizontally partitioned data. In: Proceedings of International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 239–243 (2005)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48910-X_16
Lory, P.: Enhancing the efficiency in privacy preserving learning of decision trees in partitioned databases. In: Domingo-Ferrer, J., Tinnirello, I. (eds.) PSD 2012. LNCS, vol. 7556, pp. 322–335. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33627-0_25
Peter, A., Tews, E., Katzenbeisser, S.: Efficiently outsourcing multiparty computation under multiple keys. IEEE Trans. Inf. Forensics Secur. 8(12), 2046–2058 (2013)
Samet, S., Miri, A.: Privacy preserving ID3 using Gini index over horizontally partitioned data. In: Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, pp. 645–651 (2008)
Shen, Y., Shao, H., Yang, L.: Privacy preserving C4.5 algorithm over vertically distributed datasets. In: Proceedings of IEEE International Conference on Networks Security, Wireless Communications and Trusted Computing, pp. 446–448 (2009)
Vaidya, J., Clifton, C.: Privacy-preserving decision trees over vertically partitioned data. In: Jajodia, S., Wijesekera, D. (eds.) DBSec 2005. LNCS, vol. 3654, pp. 139–152. Springer, Heidelberg (2005). https://doi.org/10.1007/11535706_11
Vaidya, J., Shafiq, B., Fan, W., et al.: A random decision tree framework for privacy-preserving data mining. IEEE Trans. Dependable Secure Comput. 11(5), 399–411 (2014)
Xiao, M.J., Han, K., Huang, L.S., et al.: Privacy preserving C4.5 algorithm over horizontally partitioned data. In: Proceedings of International Conference on Grid and Cooperative Computing, pp. 78–85 (2006)
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This work is supported by National High Technology Research and Development Program of China (No. 2015AA016008).
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Li, Y., Jiang, Z.L., Wang, X., Yiu, S.M., Fang, J. (2017). Outsourced Privacy-Preserving Random Decision Tree Algorithm Under Multiple Parties for Sensor-Cloud Integration. In: Liu, J., Samarati, P. (eds) Information Security Practice and Experience. ISPEC 2017. Lecture Notes in Computer Science(), vol 10701. Springer, Cham. https://doi.org/10.1007/978-3-319-72359-4_31
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DOI: https://doi.org/10.1007/978-3-319-72359-4_31
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