Project Abstract

Spam has been a massive issue globally ever since the inception of the internet, while the issue of spam has been greatly reduced with the help of machine-learning(ML) classifiers, there are still concerns regarding data privacy and end-to-end confidentiality when training and testing the machine learning model, to solve this issue we will be aiming to build a spam classifier with an encryption system built into it, these type of systems are widely known as privacy-preserving systems. The project has been coded using Python, and uses different ML models on the ScikitLearn library, for the encryption we used the Python Paillier library, which has homomorphic encryption properties that allow addition and multiplication operations to be done on encrypted data, so that it can be fit into the ML model.

Keywords: Machine Learning, Cryptography, Spam Identification

 Conference Details

Session: Poster Session A at Poster Stand 62

Location: Sir Stanley Clarke Auditorium at Tuesday 7th 13:30 – 17:00

Markers: Hans Ren, Benjamin Mora

Course: BSc Computer Science, 3rd Year

Future Plans: I’m looking for work