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    面向机器学习的安全外包计算研究进展

    Research Progress of Secure Outsourced Computing for Machine Learning

    • 摘要: 依靠机器学习,传统产业的数字化转型带来了海量数据增长,而产品服务的智能化提升则刺激了算力需求. 云计算的灵活资源调配可以为资源有限的企业和用户提供便宜便捷的外包计算服务,实现机器学习的模型训练和模型托管,加快产品和服务的智能化建设,促进数字经济增长. 然而,数据和模型外包伴随控制权转移,可能带来数据泄露风险和计算安全问题. 近年来,机器学习的外包安全问题受到越来越多研究者的关注,并取得了一些显著成果. 通过对2018—2022年这5年国内外机器学习安全外包研究工作调研,首先对现有主流的外包模型进行分类和特征归纳,依据任务阶段将外包模型划分为模型训练和模型托管模式,以及依据云服务商数量将外包模式划分为单云模式和多云模式. 其次重点从逻辑回归、朴素贝叶斯分类、支持向量机、决策树和神经网络等典型机器学习算法角度对机器学习安全外包计算相关研究进展进行了深入阐述和分析. 最后从不同角度分析和讨论了目前机器学习安全外包研究存在的不足,并展望未来面临的挑战和机遇.

       

      Abstract: Based on machine learning, the digital transformation of traditional industries brings a massive data growth, while the intelligent enhancement of products services raises the demand for computing power. Cloud computing, relying on flexible resource deployment, can provide inexpensive and convenient outsourced computing services for users with limited resources, enabling them to complete model training and model hosting for machine learning. It also contributes to the intelligent improvement of products and services and promotes the growth of the digital economy. However, data and model outsourcing come with a transfer of control, which may pose data leakage risk and computational security issues. In recent years, the security issues of machine learning outsourcing have received increasing public attentions and academic concerns. In this paper, we systematically reviewed the research work on machine learning security outsourcing in the year of 2018−2022 the past five years. We first present different outsourced modes, including model training and model hosting modes classified by the task phase, single-cloud and multi-cloud modes classified by the number of cloud service providers. Then we summarize the characteristics of outsourced models under different modes. Next, we focus on the research progress related to machine learning secure outsourced computing from the perspective of typical machine learning algorithms such as logistic regression, Bayesian classification, support vector machine, decision tree and neural network, and provide an in-depth description and analysis. Finally, we analyze and discuss the limitations from different perspectives, as well as potential challenges and opportunities.

       

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