论文标题
基于数据和分类服务的差异方法基于云环境中的基于隐私的机器学习模型
A Differential Approach for Data and Classification Service based Privacy-Preserving Machine Learning Model in Cloud Environment
论文作者
论文摘要
计算和存储中的大规模上涨将本地数据和机器学习应用程序驱动到云环境。业主可能不会完全信任云环境，因为它是由第三方管理的。但是，在共享数据和与多个利益相关者共享数据和分类器的同时保持隐私是一个至关重要的挑战。本文提出了一个基于差异隐私和机器学习方法的新型模型，使多个所有者能够共享其数据以进行利用，并为云环境中用户提供分类服务的分类器。为了处理所有者数据和分类器，该模型指定了各个不信任的各方之间的通信协议。拟议的模型还提供了一种可靠的机制来保留数据和分类器的隐私。对众多数据集的幼稚贝叶斯分类器进行了实验，以计算提出的模型效率。所达到的结果表明，所提出的模型具有高准确性，精确度，召回和F1得分高达94％，95％，94％和94％，并且与统一的工作相比，分别高达16.95％，20.16％，16.95％和23.33％。
The massive upsurge in computational and storage has driven the local data and machine learning applications to the cloud environment. The owners may not fully trust the cloud environment as it is managed by third parties. However, maintaining privacy while sharing data and the classifier with several stakeholders is a critical challenge. This paper proposes a novel model based on differential privacy and machine learning approaches that enable multiple owners to share their data for utilization and the classifier to render classification services for users in the cloud environment. To process owners data and classifier, the model specifies a communication protocol among various untrustworthy parties. The proposed model also provides a robust mechanism to preserve the privacy of data and the classifier. The experiments are conducted for a Naive Bayes classifier over numerous datasets to compute the proposed model efficiency. The achieved results demonstrate that the proposed model has high accuracy, precision, recall, and F1-score up to 94%, 95%, 94%, and 94%, and improvement up to 16.95%, 20.16%, 16.95%, and 23.33%, respectively, compared with state-of-the-art works.
