Nowadays, more and more machine learning applications, such as medical diagnosis, online fraud detection, email spam filtering, etc., services are provided by cloud computing. The cloud service provider collects the data from the various owners to train or classify the machine learning system in the cloud environment. However, multiple data owners may not entirely rely on the cloud platform that a third party engages. Therefore, data security and privacy problems are among the critical hindrances to using machine learning tools, particularly with multiple data owners. In addition, unauthorized entities can detect the statistical input data and infer the machine learning model parameters. Therefore, a privacy-preserving model is proposed, which protects the privacy of the data without compromising machine learning efficiency. In order to protect the data of data owners, the epsilon-differential privacy is used, and fog nodes are used to address the problem of the lower bandwidth and latency in this proposed scheme. The noise is produced by the epsilon-differential mechanism, which is then added to the data. Moreover, the noise is injected at the data owner site to protect the owners data. Fog nodes collect the noise-added data from the data owners, then shift it to the cloud platform for storage, computation, and performing the classification tasks purposes.
翻译:目前,越来越多的机器学习应用程序,如医疗诊断、在线欺诈检测、电子邮件垃圾过滤等,都由云计算来提供服务。云服务供应商收集各所有者的数据,以在云环境中对机器学习系统进行培训或分类。然而,多个数据所有者可能并不完全依赖第三方参与的云平台。因此,数据安全和隐私问题是使用机器学习工具的关键障碍,特别是多数据拥有者。此外,未经授权的实体可以检测统计输入数据,并推断机器学习模型参数。因此,提议了一个保护隐私的模式,保护数据的隐私,同时不损害机器学习效率。为了保护数据所有者的数据,使用了电子隔热隐私,并使用雾节点来解决这一拟议办法中的低频和低宽度问题。噪音是由电子隔热机制产生的,然后添加到数据中。此外,在数据所有者网站上注入噪音,以保护数据拥有者的数据数据数据。Fog nodes收集了数据所有者的数据数据,从云层存储平台向云层进行数据转换。