Nowadays, cloud computing has played a vital role in most data-intensive applications to store the data in the intermediated dataset. This effective cloud storage process helps to minimize the storage and processing cost while performing recomputing. Although the cloud provides numerous services, resources maintaining cost, outsourced user data protection from unauthorized users, the privacy of sensitive data, and computation complexity is still a major issue. A novel deep learning network-based frequent pattern mining model (DLFPM) is introduced to overcome these issues. Here, the presented method examines the frequent access information according to the layers of network functions. The network computes the sensitive information from the deep learning function. The identified sensitivity information is encrypted using the single sign-on associated with the Paillier encryption technique (SSO-PE) that avoids unauthorized access. The effective utilization of these algorithms continuously manages the sensitive data security that helps to minimize the computation cost and computation time.

如今，云计算在大多数数据密集型应用程序中发挥着至关重要的作用，将数据存储在中间数据集中。这种有效的云存储过程有助于在执行重新计算时最大限度地降低存储和处理成本。尽管云提供了大量的服务，但资源维护成本、未授权用户的外包用户数据保护、敏感数据的隐私和计算复杂性仍然是一个主要问题。引入了一种新颖的基于深度学习网络的频繁模式挖掘模型（DLFPM）来克服这些问题。这里，所提出的方法根据网络功能的层次来检查频繁访问的信息。网络通过深度学习函数计算敏感信息。所识别的敏感度信息使用与 Paillier 加密技术 (SSO-PE) 关联的单点登录进行加密，以避免未经授权的访问。这些算法的有效利用可以持续管理敏感数据的安全性，有助于最大限度地减少计算成本和计算时间。