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Accountable Ciphertext-Policy Attribute-Based Encryption Scheme Supporting Public Verifiability and Nonrepudiation Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Secure Naïve Bayesian Classification over Encrypted Data in Cloud

Authors : Xingxin Li, Youwen Zhu, Jian Wang

Published in: Provable Security

Publisher: Springer International Publishing

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Abstract

To enjoy the advantage of cloud service while preserving security and privacy, huge data is increasingly outsourced to cloud in encrypted form. Unfortunately, encryption may impede the analysis and computation over the outsourced dataset. Naïve Bayesian classification is an effective algorithm to predict the class label of unlabeled samples. In this paper, we investigate naïve Bayesian classification on encrypted dataset in cloud and propose a secure scheme for the challenging problem. In our scheme, all the computation task of naïve Bayesian classification are completed by the cloud, which can dramatically reduce the burden of data owner and users. Based on the theoretical proof, our scheme can guarantee the security of both input dataset and output classification results, and the cloud can learn nothing useful about the training data of data owner and the test samples of users throughout the computation. Additionally, we evaluate our computation complexity and communication overheads in detail.

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Appendix
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Literature
1.
Bellazzi, R., Zupan, B.: Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 77(2), 81–97 (2008)CrossRef
2.
3.
Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: The Network and Distributed System Security Symposium (NDSS), pp. 1–14 (2015)
4.
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for privacy preserving distributed data mining. ACM Sigkdd Explorations Newslett. 4(2), 28–34 (2002)CrossRef
5.
Clifton, C., Vaidya, J., Kantarcioglu, M.: Privacy-preserving naïve Bayes classification. VLDB J. 17(4), 879–898 (2008)CrossRef
6.
Dong, C., Chen, L., Camenisch, J., Russello, G.: Fair private set intersection with a semi-trusted arbiter. In: Wang, L., Shafiq, B. (eds.) DBSec 2013. LNCS, vol. 7964, pp. 128–144. Springer, Heidelberg (2013)CrossRef
7.
Elgamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31(4), 469–472 (1985)MathSciNetCrossRefMATH
8.
Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: IEEE 30th International Conference on Data Engineering (ICDE), pp. 664–675 (2014)
9.
Goldreich, O.: Foundations of Cryptography: Volume II, Basic Applications. Cambridge University Press, Cambridge (2004)CrossRefMATH
10.
Kantarcıoglu, M., Vaidya, J., Clifton, C.: Privacy preserving naive Bayes classifier for horizontally partitioned data. In: IEEE ICDM workshop on privacy preserving data mining, pp. 3–9 (2003)
11.
Kim, H.J., Kim, J.U., Ra, Y.G.: Boosting naïve Bayes text classification using uncertainty-based selective sampling. Neurocomputing 67, 403–410 (2005)CrossRef
12.
13.
Liu, A., Zhengy, K., Liz, L., Liu, G., Zhao, L., Zhou, X.: Efficient secure similarity computation on encrypted trajectory data. In: IEEE 31st International Conference on Data Engineering (ICDE), pp. 66–77 (2015)
14.
Liu, X., Lu, R., Ma, J., Chen, L., Qin, B.: Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE J. Biomed. Health Inform. 20(2), 655–668 (2016)CrossRef
15.
Lops, P., Gemmis, M.D., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Recommender Systems Handbook, pp. 73–105 (2011)
16.
Mitchell, T.: Machine Learning, 1st edn. McGraw-Hill Science/Engineering/Math, New York (1997)MATH
17.
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)
18.
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
19.
Samanthula, B.K., Jiang, W.: Efficient privacy-preserving range queries over encrypted data in cloud computing. In: IEEE Sixth International Conference on Cloud Computing, pp. 51–58 (2013)
20.
Yang, Z., Zhong, S., Wright, R.N.: Privacy-preserving classification of customer data without loss of accuracy. In: Siam International Conference on Data Mining, pp. 92–102 (2005)
21.
Yao, A.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science, pp. 162–167. IEEE (1986)
22.
Yi, X., Zhang, Y.: Privacy-preserving naive Bayes classification on distributed data via semi-trusted mixers. Inform. Syst. 34(3), 371–380 (2009)CrossRef
23.
Yuan, J., Yu, S.: Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Trans. Parallel Distrib. Syst. 25(1), 212–221 (2014)CrossRef
24.
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)CrossRef
25.
Zhu, Y., Wang, Z., Zhang, Y.: Secure k-NN query on encrypted cloud data with limited key-disclosure and offline data owner. In: The 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 401–414 (2016)
Metadata
Title
Secure Naïve Bayesian Classification over Encrypted Data in Cloud
Authors
Xingxin Li
Youwen Zhu
Jian Wang
Copyright Year
2016
Book
Provable Security

Print ISBN: 978-3-319-47421-2

Electronic ISBN: 978-3-319-47422-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47422-9_8

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