# Secure Naïve Bayesian Classification over Encrypted Data in Cloud

## 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.

## Keywords

Cloud security Naïve Bayesian classification Privacy## Notes

### Acknowledgements

We thank the anonymous reviewers and our shepherd, Prof. Xun Yi, for their valuable feedbacks. This work is partly supported by the Natural Science Foundation of Jiangsu Province of China (No. BK20150760), the Fundamental Research Funds for the Central Universities (No. NZ2015108, NS2016094), the China Postdoctoral Science Foundation funded project (No. 2015M571752), and the Natural Science Foundation of China (No. 61472470).

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