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@article{185836,
        author = {MANEPALLI SAI AASRITHA},
        title = {EMAIL BREACH CHECKER},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2892-2895},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185836},
        abstract = {Data breaches continue to rise at an alarming rate, exposing sensitive personal and organizational information. Traditional breach-detection methods often struggle with scalability, lookup efficiency and semantic categorization. To address these challenges, this paper presents a hybrid breach-checking framework that integrates a Bloom Filter–based probabilistic membership testing module with a Natural Language Processing (NLP)–based breach categorization component. The Bloom Filter significantly reduces search latency by rapidly pre-checking large datasets of compromised credentials, while the NLP classifier categorizes breaches into meaningful classes such as financial, social, or governmental. Additionally, we introduce Breach Checker, a privacy-preserving React-based application that empowers end users to verify if their email addresses have appeared in known breaches, with strong emphasis on security, usability, and performance. Experimental evaluations on real-world breach datasets demonstrate constant-time lookups (O(1)), memory efficiency under 5 MB, and categorization accuracy of 87%. The Breach Checker application further enhances user awareness through interactive visualization, local scan history management, and privacy-first design. Together, the proposed framework contributes to modern cybersecurity defence mechanisms by combining efficiency, interpretability, and user empowerment.},
        keywords = {Data Breach, Cybersecurity, Bloom Filter, NLP, Web Application, Privacy},
        month = {October},
        }