A Flexible and Investigation Approach for Encrypted Features Space Using Neural Network

Abstract

Replication, in general, is defined as repeating a study's technique and assessing whether the previous finding re-occurs. Research can become replicable when a person can copy the same content and arrive at the same conclusion as the original study. In this paper, an approach control mechanism for article observation replicas is been proposed. The usage of encrypted pictures or encoded attribute plots has been shown to be successful in preventing unwanted approach to models. The approach's efficiency has only been verified in image organization models and semantic analysis models but not in article recognition models. For the first time, encoded feature plots have proved to be successful in the control of article observation replicas. A safe and efficient technique based on completely homomorphic encryption is used and its usefulness for a variety of real data is been demonstrated. The suggested technique is the first to directly replicate an algorithm on ciphertext, which is one of the best performers on the plaintext feature selection problem. Furthermore, the suggested protocol is extensible to the scenario of more than three data owners.