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2018 | OriginalPaper | Chapter

A Review of Privacy-Preserving Machine Learning Classification

Authors : Andy Wang, Chen Wang, Meng Bi, Jian Xu

Published in: Cloud Computing and Security

Publisher: Springer International Publishing

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Abstract

Machine Learning (ML) Classification has already become one of the most commonly used techniques in many areas such as banking, medicine, spam detection and data mining applications. Often, the training of models require massive data which may contain sensitive information and the classification phase may expose the train models and the inputs from the users. Neither the models nor the train datasets and inputs should expose private information. Addressing this goal, several schemes have been proposed for privacy preserving classification. In this paper, we review those privacy preserving techiniques which applied for different machine learning classification algorithms. These algorithms conclude k-NN, SVM, Bayesian, neural networks, decision tree and etc. we sum up the comparison protocols. Finally, this work comes up with some correlative problems which are worthy to study in the future.

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Literature
1.
go back to reference Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–54 (1999)CrossRef Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–54 (1999)CrossRef
2.
go back to reference Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 2, pp. 1097–1105, Lake Tahoe, NV, United states (2012) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 2, pp. 1097–1105, Lake Tahoe, NV, United states (2012)
3.
go back to reference Kaufman, D.J., Murphy-Bollinger, J., Scott, J., Hudson, K.L.: Public opinion about the importance of privacy in biobank research. Am. J. Hum. Genet. 85(5), 643–654 (2009)CrossRef Kaufman, D.J., Murphy-Bollinger, J., Scott, J., Hudson, K.L.: Public opinion about the importance of privacy in biobank research. Am. J. Hum. Genet. 85(5), 643–654 (2009)CrossRef
4.
go back to reference Liu, F., Ng, W.K., Zhang, W.: Encrypted SVM for outsourced data mining. In: IEEE International Conference on Cloud Computing, pp. 1085–1092 (2015) Liu, F., Ng, W.K., Zhang, W.: Encrypted SVM for outsourced data mining. In: IEEE International Conference on Cloud Computing, pp. 1085–1092 (2015)
5.
go back to reference Samanthula, B.K., Elmehdwi, Y., Jiang, W.: k-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans. Knowl. Data Eng. 27(5), 1261–1273 (2015)CrossRef Samanthula, B.K., Elmehdwi, Y., Jiang, W.: k-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans. Knowl. Data Eng. 27(5), 1261–1273 (2015)CrossRef
6.
go back to reference Barthe, G., et al.: Differentially private Bayesian programming. In: ACM SIGSAC Conference on Computer and Communications Security, pp. 68–79 (2016) Barthe, G., et al.: Differentially private Bayesian programming. In: ACM SIGSAC Conference on Computer and Communications Security, pp. 68–79 (2016)
7.
go back to reference Dou, J.W., Liu, X.H., Zhou, S.F., Li, S.D.: Efficient secure multi-party computation protocol and application over set (2018) Dou, J.W., Liu, X.H., Zhou, S.F., Li, S.D.: Efficient secure multi-party computation protocol and application over set (2018)
13.
go back to reference Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the ACM Conference on Computer and Communications Security, vol. 24–28, pp. 308–318, Vienna, Austria (2016) Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the ACM Conference on Computer and Communications Security, vol. 24–28, pp. 308–318, Vienna, Austria (2016)
14.
go back to reference Abadi, M., Agarwal, A., Barham, P., Brevdo, E., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems (2016) Abadi, M., Agarwal, A., Barham, P., Brevdo, E., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems (2016)
15.
go back to reference Johnson, R., Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction. In: Advances in Neural Information Processing Systems, pp. 315–323, Lake Tahoe, NV, United states (2013) Johnson, R., Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction. In: Advances in Neural Information Processing Systems, pp. 315–323, Lake Tahoe, NV, United states (2013)
16.
go back to reference Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH
17.
go back to reference Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12, 1069–1109 (2011)MathSciNetMATH Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12, 1069–1109 (2011)MathSciNetMATH
18.
go back to reference Hardt, M., Ligett, K., McSherry, F.: A simple and practical algorithm for differentially private data release. In: Conference on Neural Information Processing Systems 2012, NIPS 2012, vol. 3, pp. 2339–2347, Lake Tahoe, NV, United states (2012) Hardt, M., Ligett, K., McSherry, F.: A simple and practical algorithm for differentially private data release. In: Conference on Neural Information Processing Systems 2012, NIPS 2012, vol. 3, pp. 2339–2347, Lake Tahoe, NV, United states (2012)
19.
go back to reference Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homomorphisms. In: Foundations of Secure Computation, pp. 169–179 (1978) Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homomorphisms. In: Foundations of Secure Computation, pp. 169–179 (1978)
20.
go back to reference Rivest, R., Shamir, A., Adleman, L.M.: A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978)MathSciNetCrossRef Rivest, R., Shamir, A., Adleman, L.M.: A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978)MathSciNetCrossRef
21.
go back to reference Goldwasser, S., Micali, S.: Probabilistic encryption & how to play mental poker keeping secret all partial information. In: Fourteenth ACM Symposium on Theory of Computing, pp. 365–377 (1982) Goldwasser, S., Micali, S.: Probabilistic encryption & how to play mental poker keeping secret all partial information. In: Fourteenth ACM Symposium on Theory of Computing, pp. 365–377 (1982)
22.
go back to reference ElGamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31, 469–472 (1985)MathSciNetCrossRef ElGamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31, 469–472 (1985)MathSciNetCrossRef
25.
go back to reference Aslett, L., Esperanca, P., Holmes, C.: A review of homomorphic encryption and software tools for encrypted statistical machine learning. Computer Science (2015) Aslett, L., Esperanca, P., Holmes, C.: A review of homomorphic encryption and software tools for encrypted statistical machine learning. Computer Science (2015)
26.
go back to reference Yu, H., Jiang, X., Vaidya, J.: Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data. In: ACM Symposium on Applied Computing, pp. 603–610 (2006) Yu, H., Jiang, X., Vaidya, J.: Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data. In: ACM Symposium on Applied Computing, pp. 603–610 (2006)
27.
go back to reference Yu, H., Vaidya, J., Jiang, X.: Privacy-preserving SVM classification on vertically partitioned data. In: Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 647–656 (2006) Yu, H., Vaidya, J., Jiang, X.: Privacy-preserving SVM classification on vertically partitioned data. In: Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 647–656 (2006)
28.
go back to reference Laur, S., Lipmaa, H.: Cryptographically private support vector machines. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 618–624 (2006) Laur, S., Lipmaa, H.: Cryptographically private support vector machines. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 618–624 (2006)
29.
go back to reference Li, P., Li, J., Huang, Z., Li, T., Gao, C.Z., Yiu, S.M., Chen, K.: Multi-key privacy-preserving deep learning in cloud computing. Futur. Gener. Comput. Syst. 74, 76–85 (2017)CrossRef Li, P., Li, J., Huang, Z., Li, T., Gao, C.Z., Yiu, S.M., Chen, K.: Multi-key privacy-preserving deep learning in cloud computing. Futur. Gener. Comput. Syst. 74, 76–85 (2017)CrossRef
30.
go back to reference Yao, A.C.: Protocols for secure computations. In: Symposium on Foundations of Computer Science, pp. 160–164 (1982) Yao, A.C.: Protocols for secure computations. In: Symposium on Foundations of Computer Science, pp. 160–164 (1982)
31.
go back to reference Malkhi, D., Nisan, N., Pinkas, B., Sella, Y.: Fairplay-a secure two-party computation system. In: Conference on USENIX Security Symposium, pp. 287–302 (2004) Malkhi, D., Nisan, N., Pinkas, B., Sella, Y.: Fairplay-a secure two-party computation system. In: Conference on USENIX Security Symposium, pp. 287–302 (2004)
32.
go back to reference Ben-David, A., Nisan, N., Pinkast, B.: Fairplaymp - a system for secure multi-party computation, pp. 257–266, Alexandria, VA, United states (2008) Ben-David, A., Nisan, N., Pinkast, B.: Fairplaymp - a system for secure multi-party computation, pp. 257–266, Alexandria, VA, United states (2008)
33.
go back to reference Henecka, W., Kogl, S., Sadeghi, A.R., Schneider, T., Wehrenberg, I.: Tasty: tool for automating secure two-party computations, pp. 451–462, Chicago, IL, United states (2010) Henecka, W., Kogl, S., Sadeghi, A.R., Schneider, T., Wehrenberg, I.: Tasty: tool for automating secure two-party computations, pp. 451–462, Chicago, IL, United states (2010)
34.
go back to reference Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: Network and Distributed System Security Symposium (2015) Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: Network and Distributed System Security Symposium (2015)
36.
go back to reference Asharov, G., Jain, A., López-Alt, A., Tromer, E., Vaikuntanathan, V., Wichs, D.: Multiparty computation with low communication, computation and interaction via threshold FHE. In: Pointcheval, D., Johansson, T. (eds.) EUROCRYPT 2012. LNCS, vol. 7237, pp. 483–501. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29011-4_29CrossRef Asharov, G., Jain, A., López-Alt, A., Tromer, E., Vaikuntanathan, V., Wichs, D.: Multiparty computation with low communication, computation and interaction via threshold FHE. In: Pointcheval, D., Johansson, T. (eds.) EUROCRYPT 2012. LNCS, vol. 7237, pp. 483–501. Springer, Heidelberg (2012). https://​doi.​org/​10.​1007/​978-3-642-29011-4_​29CrossRef
37.
go back to reference Jiang, L.Z., Xu, C.X., Wang, X.F., Chem, K.F., Wang, B.C.: The application of (fully) homomorphic encryption on ciphertext-based computational model. J. Cryptogr. (6) (2017) Jiang, L.Z., Xu, C.X., Wang, X.F., Chem, K.F., Wang, B.C.: The application of (fully) homomorphic encryption on ciphertext-based computational model. J. Cryptogr. (6) (2017)
Metadata
Title
A Review of Privacy-Preserving Machine Learning Classification
Authors
Andy Wang
Chen Wang
Meng Bi
Jian Xu
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00015-8_58

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