Abstract
With the development of social networks, there are more and more social data produced, which usually contain valuable knowledge that can be utilized in many fields, such as commodity recommendation and sentimental analysis. The SVM classifier, as one of the most prevailing machine learning techniques for classification, is a crucial tool for social data analysis. Since training a high-quality SVM classifier usually requires a huge amount of data, it is a better choice for individuals and small enterprises to conduct collaborative training with multiple parties. Nevertheless, it causes privacy risks when sharing sensitive data with untrusted people and enterprises. Existing solutions mainly adopt the computation-intensive cryptographic methods which are not efficient for practical applications. Therefore, it is an urgent and challenging task to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving nonlinear SVM classifier training scheme based on blockchain. We first design a series of secure computation protocols which can achieve secure nonlinear SVM classifier training with minimal computation overheads. Then, leveraging these building blocks, we propose a blockchain-based secure nonlinear SVM classifier training scheme that realizes collaborative training while protecting privacy. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.
Highlights
Nowadays, social networks have been playing a significant role in reflecting and influencing human living styles
It makes people be able to keep in touch with each other and share information anytime and anywhere. e development of social networks has resulted in more and more socialrelated data being produced, which consist of various raw insights and information
The results demonstrate that our scheme can achieve high accuracy and obtain nonlinear Support vector machine (SVM) classifiers with practical training efficiency
Summary
Social networks have been playing a significant role in reflecting and influencing human living styles. The homomorphic encryption technique is usually involved with computationally expensive cryptographic primitives, which result in heavy computation cost Differential privacy is another method to guarantee the security of data [7, 8]. Shen et al [9] proposed a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data Their scheme just fits linear data but cannot deal with classification tasks for nonlinear datasets, which are more common in practice. (1) To train a high-quality nonlinear SVM classifier while protecting privacy, we propose a privacypreserving training scheme based on blockchain. (2) We adopt the additive secret sharing techniques and design a series of arithmetic primitives such as multiplication, comparison, and natural exponential computation to realize efficient collaborative training while protecting the privacy of both the data and the model.
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