Abstract
Classifier has been widely applied in machine learning, such as pattern recognition, medical diagnosis, credit scoring, banking and weather prediction. Because of the limited local storage at user side, data and classifier has to be outsourced to cloud for storing and computing. However, due to privacy concerns, it is important to preserve the confidentiality of data and classifier in cloud computing because the cloud servers are usually untrusted. In this work, we propose a framework for privacy-preserving outsourced classification in cloud computing (POCC). Using POCC, an evaluator can securely train a classification model over the data encrypted with different public keys, which are outsourced from the multiple data providers. We prove that our scheme is secure in the semi-honest model
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Gu, B., Sheng, V.S.: A robust regularization path algorithm for V-support vector classification. IEEE Trans. Neural Netw. Learn. Syst. 4, 1–32 (2016). doi:10.1109/TNNLS.2016.2527796
Gu, B., Sun, X., Sheng, V.S.: Structural minimax probability machine. IEEE Trans. Neural Netw. Learn. Syst. (2016). doi:10.1109/TNNLS.2016.2544779
Wen, X.Z., Shao, L., Xue, Y., Fang, W.: A rapid learning algorithm for vehicle classification. Inf. Sci. 295(1), 395–406 (2015)
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 2013, pp. 6645–6649. IEEE (2013)
Gupta, B.B., Badve, O.P.: Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a cloud computing environment. Neural Comput. Appl. 2016, 1–28 (2016)
Stergiou, C., Psannis, K.E., Kim, B.G., et al.: Secure integration of IoT and cloud computing. Futur. Gener. Comput. Syst. (2016). doi:10.1016/j.future.2016.11.031
Li, J., Li, J.W., Chen, X.F., et al.: Identity-based encryption with outsourced revocation in cloud computing. IEEE Trans. Comput. 64(2), 425–437 (2015)
Li, J., Yan, H.Y., Liu, Z.L., et al.: Location-sharing systems with enhanced privacy in mobile online social networks. IEEE Syst. J. (2015)
Li, J., Chen, X.F., Huang, X.Y., et al.: Secure distributed deduplication systems with improved reliability. IEEE Trans. Comput. 64(12), 3569–3579 (2015)
Badve, O.P., Gupta, B.B.: Taxonomy of recent DDoS attack prevention, detection, and response schemes in cloud environment. In: Proceedings of the International Conference on Recent Cognizance in Wireless Communication and Image Processing, pp. 683–693. Springer, Delhi (2016)
Gou, Z., Yamaguchi, S., Gupta, B.B.: Analysis of various security issues and challenges in cloud computing environment: a survey. In: Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security, pp. 393–419. IGI Global (2016)
Xia, Z.H., Wang, X.H., Sun, X.M., et al.: A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. 27(2), 340–352 (2015)
Fu, Z.J., Huang, F.X., Sun, X.M. et al.: Enabling semantic search based on conceptual graphs over encrypted outsourced data. IEEE Trans. Serv. Comput. (2016)
Fu, Z.J., Ren, K., Shu, J.G., et al.: Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans. Parallel Distrib. Syst. 27(9), 2546–2559 (2016)
Erkin, Z., Franz, M., Guajardo, J., et al.: Privacy-preserving face recognition. In: International Symposium on Privacy Enhancing Technologies Symposium, pp. 235–253. Springer, Berlin (2009)
Yuan, J.W., Yu, S.C.: Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Trans. Parallel Distrib. Syst. 25(1), 212–221 (2014)
Boneh, D., Goh, E.J., Nissim, K.: Evaluating 2-DNF formulas on ciphertexts. In: Theory of Cryptography Conference, pp. 325–341. Springer, Heidelberg (2005)
Zhang, Q., Yang, L.T., Chen, Z.: Privacy preserving deep computation model on cloud for big data feature learning. IEEE Trans. Comput. 65(5), 1351–1362 (2016)
Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrapping. ACM Trans. Comput. Theory (TOCT) 6(3), 13 (2014)
Gentry, C.: Fully homomorphic encryption using ideal lattices. STOC 2009(9), 169–178 (2009)
Goldreich, O.: Secure multi-party computation. Manuscript. Preliminary version, pp. 86–97 (1998)
Van Dijk, M.. Gentry, C., Halevi, S. et al.: Fully homomorphic encryption over the integers. In: Annual International Conference on the Theory and Applications of Cryptographic Techniques, pp. 24–43. Springer, Berlin (2010)
Yao, A.C.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science, vol. 1986, 162–167. IEEE (1986)
Schlitter, N.: A protocol for privacy preserving neural network learning on horizontally partitioned data. PSD (2008)
Agrawal, D., Srikant, R.: Privacy-preserving data mining. Proc. ACM Conf. Manag. Data 29(2), 439–450 (2000)
Vaidya, J., Kantarcoǧlu, M., Clifton, C.: Privacy-preserving naive bayes classification. VLDB J. Int. J. Very Large Data Bases 17(4), 879–898 (2008)
Samanthula, B.K., Rao, F.Y., Bertino, E., et al.: Privacy-Preserving and Outsourced Multi-User k-Means Clustering. arXiv preprint arXiv:1412.4378 (2014)
Jagannathan, G., Wright, R.: Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 593–599. ACM (2005)
Lin, K.P.: Privacy-preserving kernel k-means clustering outsourcing with random transformation. Knowl. Inf. Syst. 49(3), 885–908 (2016)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639–644. ACM (2002)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. ACM Sigmod Record. ACM 29(2), 439–450 (2000)
Dankar, F.K.: Privacy preserving linear regression on distributed databases. Trans. Data Priv. 8(1), 3–28 (2015)
Dankar, F., Brien, R., Adams, C., Matwin, S.: Secure multi-party linear regression. In: EDBT/ICDT Workshops, p. 406414 (2014)
Vaidya, J., Clifton, C., Kantarcioglu, M., et al.: Privacy-preserving decision trees over vertically partitioned data. ACM Trans. Knowl. Discov. from Data (TKDD) 2(3), 14 (2008)
Bansal, A., Chen, T., Zhong, S.: Privacy preserving back-propagation neural network learning over arbitrarily partitioned data. Neural Comput. Appl. 20(1), 143–150 (2011)
Chen, T.T., Zhong, S.: Privacy-preserving back-propagation neural network learning. IEEE Trans. Neural Netw. 20(10), 1554–1564 (2009)
Graepel, T., Lauter, K., Naehrig, M.: ML confidential: Machine learning on encrypted data. Information Security and Cryptology (ICISC), p. 121. Springer, Berlin (2012)
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321. ACM (2015)
Barni, M., Failla, P., Lazzeretti, R.: Efficient privacy-preserving classification of ECG signals. In: First IEEE International Workshop on, Information Forensics and Security, et al.: WIFS 2009, pp. 91–95. IEEE (2009)
Liu, X., Lu, R., Ma, J., et al.: Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE J. Biomed. Health Inf. 20(2), 655–668 (2016)
Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. NDSS (2015)
Zhang, T., Zhu, Q.: Dynamic differential privacy for ADMM-based distributed classification learning. IEEE Trans. Inf. Forensics Secur. 12(1), 172–187 (2017)
Di Vimercati, S.D.C., Foresti, S., Jajodia, S., et al.: Over-encryption: management of access control evolution on outsourced data. In: Proceedings of the 33rd International Conference on Very Large Data Bases. VLDB endowment, pp. 123–134 (2007)
Goldwasser, S., Micali, S.: Probabilistic encryption. J. Comput. Syst. Sci. 28(2), 270–299 (1984)
Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 61472091), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (2014A030306020), and Science and Technology Planning Project of Guangdong Province, China (2015B010129015)
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Li, P., Li, J., Huang, Z. et al. Privacy-preserving outsourced classification in cloud computing. Cluster Comput 21, 277–286 (2018). https://doi.org/10.1007/s10586-017-0849-9
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DOI: https://doi.org/10.1007/s10586-017-0849-9