In the age of security around data and machine learning, it is important to protect that data while enabling means to effectively analyze it.This paper proposes a secure framework that combines cryptographic data protection with deep learning techniques, allowing meaningful knowledge extraction without sacrificing data security. A CMAP authentication protocol is proposed for securing data exchange on federated cloud servers and brings together cryptography, machine learning, and statistical models to contextualize data. The cryptographic algorithm selected for data exchange was the DES (Data Encryption Standard) algorithm for encryption. Data preparation for neural network and deep learning processing included techniques such as one-hot encoding, tokenization, stemming, stop-word removal, and normalization. The various available deep learning models (CNN, LSTM, and GRU) were evaluated for predictive analysis and data classification; the data was defined as binary to ensure compatibility with numerous machine learning algorithms.This paper highlights that merging cryptographic algorithms with machine learning is achievable while positively affecting the relationship between data privacy and analysis.