Project Abstract

Spam, in its myriad forms, has become an enduring problem in the digital realm, inundating email inboxes, social media feeds, and messaging platforms with unwanted and often malicious content. The exponential growth of online communication channels has exacerbated this issue, necessitating robust mechanisms for spam identification to safeguard user privacy and security. In response to this pressing challenge, our project endeavors to harness the power of machine learning in tandem with homomorphic encryption to develop a novel solution for spam detection and mitigation.The project aims to create an intelligent system capable of autonomously identifying and filtering out spam content across various online platforms. Leveraging advancements in machine learning algorithms, it seek to train models that can discern patterns and features characteristic of spam messages, enabling accurate classification in real-time. Moreover, to address concerns regarding the privacy and security of user data, we propose the integration of homomorphic encryption techniques into our solution.Homomorphic encryption, a revolutionary concept in cryptography, allows for computations to be performed on encrypted data without the need for decryption, thus preserving the confidentiality of sensitive information. By applying homomorphic encryption to our machine learning models and data pipelines, we aim to fortify the entire spam identification process against potential attacks and breaches (Akinyelu, 2021).The project majorly aspires to not only enhance the efficacy of spam detection but also prioritize user privacy and data security in an increasingly interconnected digital landscape. By combining the strengths of machine learning and homomorphic encryption, we envision a comprehensive solution that empowers users to reclaim control over their online experiences, free from the disruptions and threats posed by spam. Through rigorous experimentation and evaluation, we endeavor to demonstrate the feasibility and effectiveness of our approach, paving the way for future advancements in the realm of spam identification and cybersecurity (Hussain et al. 2020).

Keywords: Robust Solution for Spam Detection, Enhanced Privacy and Security Measures, Empowering Users with Proactive Protection

 Conference Details

Session: Presentation Stream 15 at Presentation Slot 9

Location: GH029 at Wednesday 8th 09:00 – 12:30

Markers: Anton Setzer, George Brooks (GTA)

Course: MSc Computer Science, Masters PG

Future Plans: I’m looking for an industry placement