Abstract
In outsourced classification services, classifier owners outsource their classifiers to remote servers due to resource constraints, and users can request classification services from this server. What attracts us is that the users’ query data, classification results, and classifier privacy are all well protected during classification. However, we introduce a threat model that makes it easy for adversaries to attack. Thus, to ensure its security, this model should be modified. In addition, considering the low efficiency of Paillier cryptosystem, the classification phase is accompanied by problems of low computational efficiency and large occupied bandwidth consumption. In this paper, we use a substitutive OU cryptosystem, which effectively saves computational and communication costs. Moreover, experimental results show that the improvement enhances the efficiency of the scheme and reduces the bandwidth consumption.
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Acknowledgements
The authors thank the anonymous reviewers for their valuable suggestions and comments, which help the authors improve their work. This work is supported by the National Key R&D Program of China under Grant No. 2017YFB0802000, the National Natural Science Foundation of China under Grant Nos. U19B2021, U1736111, the National Cryptography Development Fund under Grant No.MMJJ20180111, the Key Research and Development Program of Shaanxi under Grant No. 2020ZDLGY08-04, the Fundamental Research Funds for the Central Universities, the Innovation Fund of Xidian University under Grant No. 5001-20109195456, and the Program for Science and Technology Innovation Talents in the Universities of Henan Province under Grant No. 18HASTIT022.
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Chai, Y., Zhan, Y., Wang, B. et al. Improvement on a privacy-preserving outsourced classification protocol over encrypted data. Wireless Netw 26, 4363–4374 (2020). https://doi.org/10.1007/s11276-020-02329-9
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DOI: https://doi.org/10.1007/s11276-020-02329-9