International Journal of Computing and Mathematics

Computational processing using cloud computing has increasingly become common for big data processing, and several usages of cloud resources have machine learning frameworks where different analyses and classifiers could be done rather quickly. In another, the processing of privacy big data on cloud providers continues to be a major challenge as its loss of sensitive data is a serious concern. As a solution for this, this paper suggests an Extreme Learning Machine (ELM) algorithm for privacy-preserving that would analyze data authenticated with linearly encryption techniques. The computing infrastructure is made up of a person, a cloud that holds an owner's encrypted information, and a trustworthy third party transferring the randomized multi keys. In terms of intelligence, extension, energy, and also the encryption process, this creative solution is often simpler than the encryption algorithm. It also includes the confidentiality of multi-keys. As ELM will know about encrypted data in the proposed automation framework, it is supposed to overcome an obstacle to interacting with confidential data on cloud providers. Moreover, the proposed ELM supports one to know in a secure environment from different perspectives of private data, and this may contribute to a greater realistic specific task than ever.