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Distributed Machine Learning Oriented Data Integrity Verification Scheme in Cloud Computing Environment
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DOI：10.1109/ACCESS.2020.2971519.png)
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摘要
En 中文
Distributed Machine Learning (DML) is one of the core technologies for Artificial Intelligence (AI). However, in the existing distributed machine learning framework, the data integrity is not taken into account. If network attackers forge the data, modify the data, or destroy the data, the training model in the distributed machine learning system will be greatly affected, and the training results are led to be wrong. Therefore, it is crucial to guarantee the data integrity in the DML. In this paper, we propose a distributed machine learning oriented data integrity verification scheme (DML-DIV) to ensure the integrity of training data. Firstly, we adopt the idea of Provable Data Possession (PDP) sampling auditing algorithm to achieve data integrity verification so that our DML-DIV scheme can resist forgery attacks and tampering attacks. Secondly, we generate a random number, namely blinding factor, and apply the discrete logarithm problem (DLP) to construct proof and ensure privacy protection in the TPA verification process. Thirdly, we employ identity-based cryptography and two-step key generation technology to generate data owner's public/private key pair so that our DML-DIV scheme can solve the key escrow problem and reduce the cost of managing the certificates. Finally, formal theoretical analysis and experimental results show the security and efficiency of our DML-DIV scheme.
Keyword:
Distributed machine learning
public auditing
data integrity
bilinear mapping
identity-based cryptography
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
A Secure and Efficient Data Integrity Verification Scheme for Cloud-IoT Based on Short Signature.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
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