Abstract
In this paper, we use secure multi-party computation to protect privacy. Based on consensus-based distributed support vector machines, we present a new consensus-based privacy-preserving algorithm to conduct secure multi-party computation. The proposed algorithm run in parallel at each iteration, which reduce the running time. Furthermore, what needed to be communicated at each iteration is only a coefficient vector, therefore privacy is protected to the uttermost. The algorithm is proved to be convergent globally. Numerical experiments demonstrate the feasibility and efficiency of the new algorithm.
This work was supported in part by NSFC (Grant No. 11626143) and Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (No. 2015RCJJ056).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lu, Y., Roychowdhury, V., Vandenberghe, L.: Distributed parallel support vector machines in strongly connected networks. IEEE Trans. Neural Netw. 19, 1167–1178 (2008)
Navia-Vazquez, A., Gutierrez-Gonzalez, D., Parrado-Hernandez, E., Navarro-Abellan, J.J.: Distributed support vector machines. IEEE Trans. Neural Netw. 17, 1091–1097 (2006)
Forero, P.A., Cano, A., Giannakis, G.B.: Consensus-based distributed support vector machines. J. Mach. Learn. Res. 11, 1663–1707 (2010)
Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. J. Privacy Confidentiality 1, 59–98 (2009)
Zhang, Y., Zhong, S.: A privacy-preserving algorithm for distributed training of neural network ensembles. Neural Comput. Appl. 22, 269–282 (2013)
Wang, L., Shi, J.J., Chen, C., Zhong, S.: Privacy-preserving face detection based on linear and nonlinear kernels. In: Multimedia Tools and Applications, pp. 1–21 (2017)
Forero, P.A., Cano, A., Giannakis, G.B.: Consensus-based K-means algorithm for distributed learning using wireless sensor networks. In: Proceedings of Workshop on Sensors, Signal and Information Processing, Sedona, AZ, May 2008
Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation: Numerical Methods. Athena Scientific, Massachusetts (1997)
Asuncion, A., Newman, D.J.: Statlog training set. http://www.csie.ntu.edu.tw/cjlin/libsvmtools/datasets/
Flouri, K., Beferull-Lozano, P., Tsakalides, P.: Training a support-vector machine-based classifier in distributed sensor networks. In: Proceedings of 14th European Signal Processing Conference, Florence, Italy, 4–8 September 2006
Flouri, K., Beferull-Lozano, P., Tsakalides, P.: Distributed consensus algorithm for SVM training in wireless sensor networks. In: Proceedings of 16th European Signal Processing Conference, Laussane, Switzerland, 25–29 August 2008
Bennati, S., Jonker, C.M.: PriMaL: a privacy-preserving machine learning method for event detection in distributed sensor networks (2017)
Mangasarian, O.L., Wild, E.W., Fung, G.M.: Privacy-preserving classification of horizontally partitioned data via random kernels. In: Proceedings of the 2008 International Conference on Data Mining, vol. 2, pp. 473–479 (2008)
Ruan, M., Wang, Y.: Secure and privacy-preserving average consensus (2017)
Sagastizabal, C.A., Solodov, M.V.: Parallel variable distribution for constrained optimization. Comput. Optim. Appl. 22, 111–131 (2002)
Ceccarello, M., Pietracaprina, A., Pucci, G., et al.: A practical parallel algorithm for diameter approximation of massive weighted graphs. In: IEEE International Parallel and Distributed Processing Symposium, pp. 12–21 (2016)
Do, T.N., Nguyen, V.H., Poulet, F.: GPU-based parallel SVM algorithm. J. Front. Comput. Sci. Technol. 3, 368–377 (2009)
Dai, Y.H., Fletcher, R.: New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds. Math. Program. 20, 403–421 (2006)
Solodov, M.V.: New inexact parallel variable distributed algorithm. Comput. Optim. Appl. 7, 165–182 (1997)
Zanni, L.: An improved gradient projection-based decomposition technique for support vector machines. CMS 3, 131–145 (2006)
Huang, M., Chen, Y., Chen, B.W., et al.: A semi-supervised privacy-preserving clustering algorithm for healthcare. Peer-to-Peer Netw. Appl. 9, 864–875 (2016)
Canetti, R.: Security and composition of multi-party cryptographic protocols. J. Cryptology 13, 143–202 (2000)
Zhang, M.L., Han, M., Wang, Z.Z.: Inter-systems privacy-preserving recommendation algorithm based on security multi-party computation. J. Hebei Univ. Technol. (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, H., Xu, F. (2019). Consensus-Based Privacy-Preserving Algorithm. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_203
Download citation
DOI: https://doi.org/10.1007/978-981-10-6571-2_203
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6570-5
Online ISBN: 978-981-10-6571-2
eBook Packages: EngineeringEngineering (R0)