Secure Text Transfer Using Diffie Hellman Key Exchange Based on Cloud

A cloud application that sends encrypted text messages. The two users agree on a private key using Diffie-Hellman key exchange, so intercepted messages cannot be read.

Text is collected from the source (reviews, posts, messages or documents). It is cleaned: lowercased, stop words removed, tokenised and lemmatised. The text is converted to features with TF-IDF or embeddings and a classifier or language model processes it. Results such as sentiment, category or a reply are shown on a dashboard or chat interface.

Lowercasing, removing punctuation and stop words, tokenisation and stemming or lemmatisation. The cleaned text is then converted to numbers with bag-of-words, TF-IDF or embeddings.

Term frequency–inverse document frequency gives a word a high weight if it appears often in one document but rarely across all documents, so common words like 'the' get low importance.

Symmetric encryption (AES, DES) uses one shared key for encryption and decryption and is fast. Asymmetric encryption (RSA) uses a public key to encrypt and a private key to decrypt, which solves key sharing but is slower.

A hash is one-way, so even the administrator cannot recover the password. We add a random salt and use a slow hash such as bcrypt so stolen hashes are hard to crack.

IaaS provides virtual machines and storage (AWS EC2), PaaS provides a platform to deploy code without managing servers (Heroku, App Engine), and SaaS is complete software used over the internet (Gmail).

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.