Abstract
Machine learning classification and pattern recognition are widely used in various scenarios nowadays, such as medical diagnosis and face recognition, both need to estimate the similarity measure between different samples. In such applications, it is critical to protect the privacy of both the private input data and the machine learning model. In this paper, we propose a privacy-preserving Mahalanobis distance scheme for the statistic pattern recognition, and construct a protocol for the privacy-preserving prediction phase by using the labeled-homomorphic encryption scheme, which combines linearly homomorphic encryption and pseudo-random function. And we consider an outsouring scenario, which most work to be outsourced to the cloud server. Our design goal is to ensure that the client’s private data is permanently confidential and protect the secret model in cloud server. Most of the previous work proposed complex schemes with too many interactions between the clients and cloud server, and we propose an efficient scheme to minimal the complexity of the client side.
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Acknowledgements
This work is supported by NSFC (61602210), Science and Technology Project of Guangzhou City (No. 201707010320), Natural Science Foundation of Guangdong Province (No. 2014A030310156), the Fundamental Research Funds for the Central Universities (21617408), the Science and Technology Planning Project of Guangdong Province, China (2014A040401027, 2015A030401043, 2017A040405029).
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Wang, Q., Zhou, D., Guan, Q., Li, Y., Yang, J. (2018). A Privacy-Preserving Classifier in Statistic Pattern Recognition. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_45
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