PSLP: Privacy-preserving single-layer perceptron learning for e-Healthcare
Guoming Wang, Rongxing Lu, Cheng Huang · 2015
In big data era, the explosive data mining techniques are used as popular tools to mine useful knowledge for the hospitals. However, considering the complexity of these techniques, the hospitals tend to outsource both data and calculations to computationally powerful cloud, which however poses a potential threat to user's privacy. In this paper, in order to address the privacy challenge, based on Paillier homomorphic cryptosystem, we propose a feasible privacy-preserving single-layer perceptron scheme, named PSLP. Specifically, in the proposed PSLP scheme, a hospital outsources the sensitive medical information to the cloud in ciphertext, and then the cloud can execute the privacy-preserving neural network training to obtain the disease model. Detailed security analysis shows the proposed PSLP can really achieve privacy-preserving property. In addition, extensive performance evaluations also demonstrate it is feasible in terms of computational cost and communication overhead.