Abstract
In multi-label (ML) learning, each training instance is associated with a set of labels to present its multiple semantic information, and the task is to predict the associated labels for each unclassified instance. Nowadays, many multi-label learning approaches have been proposed, unfortunately, all of the existing approaches did not consider the issue of protecting the privacy information. In this paper, we propose a scheme for secure multi-label classification over encrypted data in cloud. Our scheme can outsource the multi-label classification task to the cloud servers which dramatically reduce the storage and computation burden of data owner and data users. Based on the theoretical proof, our scheme can protect the privacy information of data owner and data users, the cloud servers can not learn anything useful about the input data and output multi-label classification results. Additionally, we evaluate our computation complexity and communication overheads in detail.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognit. 37(9), 1757–1771 (2004)
Cheng, C., Lu, R., Petzoldt, A., Takagi, T.: Securing the internet of things in a quantum world. IEEE Commun. Mag. 55(2), 116–120 (2017)
Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: De Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS, vol. 2168, pp. 42–53. Springer, Heidelberg (2001). doi:10.1007/3-540-44794-6_4
Elisseeff, A.E., Weston, J.: A kernel method for multi-labelled classification. Adv. Neural Inf. Process. Syst. 14, 681–687 (2002)
Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 664–675. IEEE (2014)
Freund, Y., Schapire, R., Abe, N.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14, 771–780 (1999)
Goldreich, O.: Foundations of Cryptography II: Basic Applications. Cambridge University Press, New York (2004)
Goldwasser, S., Micali, S., Rackoff, C.: The knowledge complexity of interactive proof-systems. SIAM J. Comput. 18(1), 186–208 (1989)
Hu, S., Qian, W., Wang, J., Zhan, Q., Ren, K.: Securing SIFT: privacy-preserving outsourcing computation of feature extractions over encrypted image data. IEEE Trans. Image Process. 25(7), 3411–3425 (2016)
Kantarcoglu, M., Vaidya, J.: Privacy preserving naive bayes classifier for horizontally partitioned data. In: IEEE ICDM Workshop on Privacy Preserving Data Mining, pp. 3–9 (2003)
Li, X., Zhu, Y., Wang, J.: Secure naïve bayesian classification over encrypted data in cloud. In: Chen, L., Han, J. (eds.) ProvSec 2016. LNCS, vol. 10005, pp. 130–150. Springer, Cham (2016). doi:10.1007/978-3-319-47422-9_8
Li, X., Zhu, Y., Wang, J.: Efficient encrypted data comparison through a hybrid method. J. Inf. Sci. Eng. 33(4), 953–964 (2017)
Mccallum, A.K.: Multi-label text classication with a mixture model trained by EM. In: AAAI Workshop on Text Learning, pp. 1–7 (1999)
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). doi:10.1007/3-540-48910-X_16
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011)
Rnkranz, J., Llermeier, E., Menc, L., Eneldo, A., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)
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)
Schapire, R.E., Singer, Y.: Boostexter: a boosting-based system for text categorization. Mach. Learn. 39(2), 135–168 (2000)
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS, vol. 4701, pp. 406–417. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74958-5_38
Ueda, N.: Parametric mixture models for multi-labeled text. In: Advances in Neural Information Processing Systems, pp. 721–728 (2002)
Wang, S., Nassar, M., Atallah, M., Malluhi, Q.: Secure and private outsourcing of shape-based feature extraction. In: Qing, S., Zhou, J., Liu, D. (eds.) ICICS 2013. LNCS, vol. 8233, pp. 90–99. Springer, Cham (2013). doi:10.1007/978-3-319-02726-5_7
Yi, X., Zhang, Y.: Privacy-preserving naive bayes classification on distributed data via semi-trusted mixers. Inf. Syst. 34(3), 371–380 (2009)
Zhang, L., Jung, T., Liu, C., Ding, X., Li, X.Y., Liu, Y.: POP: privacy-preserving outsourced photo sharing and searching for mobile devices. In: IEEE International Conference on Distributed Computing Systems, pp. 308–317 (2015)
Zhang, M.-L., Zhou, Z.-H.: A k-nearest neighbor based algorithm for multi-label classification. In: IEEE International Conference on Granular Computing, vol. 2, pp. 718–721 (2005)
Zhang, M.-L., Zhou, Z.-H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338–1351 (2006)
Zhou, L., Zhu, Y., Castiglione, A.: Efficient k-NN query over encrypted data in cloud with limited key-disclosure and offline data owner. Comput. Secur. 69, 84–96 (2017)
Zhu, Y., Huang, Z., Takagi, T.: Secure and controllable k-NN query over encrypted cloud data with key confidentiality. J. Parallel Distrib. Comput. 89, 1–12 (2016)
Acknowledgments
This work is partly supported by the National Key Research and Development Program of China (No. 2017YFB0802300), the Natural Science Foundation of China (No. 61602240), the Natural Science Foundation of Jiangsu Province of China (No. BK20150760), the Research Fund of Guangxi Key Laboratory of Trusted Software (No. kx201611), and the Foundation of Graduate Innovation Center in NUAA (No. kfjj20161605).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, Y., Li, X., Zhu, Y., Wang, J., Liu, Z. (2017). Secure Multi-label Classification over Encrypted Data in Cloud. In: Okamoto, T., Yu, Y., Au, M., Li, Y. (eds) Provable Security. ProvSec 2017. Lecture Notes in Computer Science(), vol 10592. Springer, Cham. https://doi.org/10.1007/978-3-319-68637-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-68637-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68636-3
Online ISBN: 978-3-319-68637-0
eBook Packages: Computer ScienceComputer Science (R0)