A Blockchain-Based Application for Authenticating Social Profiles and Preventing Fraud

Abstract

In this blockchain-based application designed to combat fake social profiles, the integration of the Naive Bayes algorithm adds a sophisticated layer of fraud detection. Leveraging blockchain's decentralized nature, user profiles are stored securely, with each profile represented as a block in the chain. Prior to inclusion in the blockchain, profiles undergo a rigorous verification process, employing various authentication methods like government-issued IDs or trusted social media accounts. Once submitted, the Naive Bayes algorithm steps in, extracting pertinent profile features such as images, usernames, and activity histories. Trained on labeled data encompassing both genuine and fake profiles, the algorithm assigns probability scores to new profiles, indicating the likelihood of authenticity. A predefined threshold determines whether a profile is deemed genuine or flagged as suspicious. The blockchain's consensus mechanism validates additions to the chain, ensuring only verified profiles enter the network. Additionally, users play a vital role through community reporting, aiding in the identification of suspicious profiles. Continuous monitoring and periodic algorithm updates uphold the system's effectiveness against evolving fraudulent tactics. Transparency and accountability remain core tenets, with verification criteria and activities recorded transparently on the blockchain, fostering trust among users. Through this amalgamation of blockchain and machine learning, the application offers a robust solution to comb at the prolife ration of fake social profiles while upholding user privacy and decentralization.