Efficient and privacy-preserving single-layer perceptron learning scheme in cloud computing environment

Abstract

The invention belongs to the technical field of cloud computing and discloses an efficient and privacy-preserving single-layer perceptron learning scheme in a cloud computing environment. The scheme comprises the steps that a client provides a security parameter, operates a key generation algorithm of a symmetric homomorphic encryption algorithm to calculate a public parameter and a key, then operates an encryption algorithm, encrypts training data through utilization of the key to obtain a corresponding ciphertext, and sends the ciphertext and related expectation to a cloud server, assists acloud server to judge a positive or negative characteristic of a dot product result in a training process, and decrypts the ciphertext of the received final optimum weight vector after a training taskis finished, thereby obtaining a single-layer perceptron prediction model; and the cloud server stores the training mode, trains a single-layer perceptron model and sends the ciphertext of the finaloptimum weight vector to the client after the training task is finished. The safety analysis shows that according to the scheme, in the training process, the privacy of the training data, an intermediate result and the optimum prediction model can be preserved, and the scheme is efficient in computing overhead and communication overhead aspects.

Description

technical field [0001] The invention belongs to the technical field of cloud computing, and in particular relates to a high-efficiency and privacy-protecting single-layer perceptron learning method in a cloud computing environment. Background technique [0002] Cloud computing is the integration and development of grid computing, parallel computing and distributed computing. It can provide not only software services, but also hardware services, which can provide convenience for storing data and improve the efficiency of data processing. Therefore, users with resource-constrained devices often outsource their data and massive computing tasks to cloud servers in a pay-as-you-go manner. However, the transaction processing of cloud computing relies heavily on virtual clouds, which may be subject to malicious attacks. In addition, the openness and heterogeneity of the network will inevitably bring security problems to data stored on cloud servers. Therefore, the outsourcing pa...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the current privacy-preserving single-layer perceptron learning method cannot protect the privacy of training data and weight vectors; the computational overhead and communication overhead are large; the lack of scalability

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