A deep study of analysis for encryption and decryption algorithm in cloud data with machine learning techniques

An emergence of cloud computing has made secure search on encrypted cloud data a popular area of study. Previous techniques’ limited ability to generate query trapdoors made them less effective at ensuring query secrecy. A data user can also readily examine the query results of another data user in these systems, as the data owner often has complete knowledge of the query results of the data users. In certain application settings, the data user could be reluctant to give away the privacy of their query to anybody but themselves. It provide a search technique that enhances privacy by letting the data user create a different random query trapdoor each time and also it suggest the security of our scheme and show through extensive experiments that it is exactly right. Put the suggested plan into practice and evaluate how well it performs in terms of key generation process, secure indexing, trapdoor creation, and search timing. Compared to current hashing and attribute-based encryption searchable encryption technologies, the suggested scheme outperforms them. So, contributed to plan into practice, assess, and contrast its results using the example of searchable algorithms for encryption