Security and privacy are useful concerns in the context of big data. The Internet of Things (IoT) serves both to bolster and to ease security worries. IoT gadgets raise immense new security challenges, particularly with regards to things like basic framework. Be that as it may, they additionally offer approaches to help keep clients progressively secure by adding additional obstructions of safeguard to information and people. In order to sustain the integrity of data and to provide in order to implicit security for any big database, data slicing is constructive. Data slicing implicitly provides the preservation and the query performance to the database users. The sliced data are stored at servers in a distributed system to protect the data from the attackers. In this article, an intelligent and efficient model is developed to partition the polynomial data securely and to store at various servers in a distributed system. An auto-key generator spawns an encryption key to encrypt the polynomial data as a higher level security. Encrypted data is partitioned by an efficient Fast Fourier transform Technique. A novel clustering methodology entitled as Binary Reverse Clustering is introduced to optimize the performance as well as to reduce the servers’ requisition. Moreover, the novel clustering technique is compared with the traditional clustering algorithm.

安全和隐私是大数据环境中有用的关注点。物联网（IoT）既可以增强安全性，也可以缓解其安全隐患。物联网小工具带来了巨大的新安全挑战，尤其是在诸如基本框架等方面。即便如此，它们还提供了一些方法，通过增加对信息和人员的保护措施来帮助客户逐步保持安全。为了维持数据的完整性并为任何大型数据库提供隐式安全性，数据切片是建设性的。数据切片隐式地为数据库用户提供了保留和查询性能。切片的数据存储在分布式系统中的服务器上，以保护数据免受攻击者的攻击。在这篇文章中，开发了一种智能，高效的模型来安全地划分多项式数据，并存储在分布式系统中的各种服务器上。自动密钥生成器会生成一个加密密钥，以加密多项式数据，以提供更高级别的安全性。通过高效的快速傅立叶变换技术对加密的数据进行分区。引入了一种名为“二进制反向集群”的新颖集群方法，以优化性能并减少服务器的需求。此外，将新颖的聚类技术与传统的聚类算法进行了比较。引入了一种名为“二进制反向集群”的新颖集群方法，以优化性能并减少服务器的需求。此外，将新颖的聚类技术与传统的聚类算法进行了比较。引入了一种名为“二进制反向集群”的新颖集群方法，以优化性能并减少服务器的需求。此外，将新颖的聚类技术与传统的聚类算法进行了比较。