论文标题
云环境中保护隐私的外包数据模型
A Privacy-Preserving Outsourced Data Model in Cloud Environment
论文作者
论文摘要
如今，越来越多的机器学习应用程序，例如医学诊断，在线欺诈检测，电子邮件垃圾邮件过滤等，由云计算提供服务。云服务提供商从各个所有者中收集数据，以在云环境中训练或对机器学习系统进行分类。但是，多个数据所有者可能不完全依赖第三方参与的云平台。因此，数据安全性和隐私问题是使用机器学习工具的关键障碍，尤其是在多个数据所有者中。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，提出了一个隐私性模型，该模型在不损害机器学习效率的情况下保护数据的隐私。为了保护数据所有者的数据，使用Epsilon-Differential隐私，并使用FOG节点来解决此提出的方案中较低带宽和潜伏期的问题。噪声是由epsilon-differential机制产生的，然后将其添加到数据中。此外，在数据所有者站点上注入噪声以保护所有者数据。 FOG节点从数据所有者那里收集噪音添加的数据，然后将其转移到云平台以进行存储，计算和执行分类任务的目的。
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.
