Privacy-preserving over encrypted data for web page phishing detection

It tricked users into disclosing sensitive information through web page phishing. A traditional phishing detection method involves analyzing web pages and user behaviour using machine learning models, which require access to raw data, raising privacy concerns. Using homomorphic encryption and multiparty computation, this paper protects privacy in phishing detection. Security and privacy are enhanced by encrypting sensitive user data during the detection process. With this approach, Extreme Learning Machine (ELM) classification is combined with TF-IDF feature selection to detect phishing while maintaining optimal computational efficiency. According to our experimental results, this approach provides an accuracy of 93.5% and has low computational and communication costs, making it a viable solution for secure real-time phishing detection.