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@article{207094,
        author = {Divyansh Mishra and Gourav Kumar and Suhani Bhardwaj},
        title = {A Privacy-Preserving AI-Integrated Blockchain Authentication System Using Zero-Knowledge Proofs},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {1-9},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207094},
        abstract = {In recent years, traditional password-based authentication systems have become increasingly vulnerable to a wide range of cyber threats, including phishing attacks, brute-force attempts, credential stuffing, and large-scale database breaches. These systems typically rely on centralized storage of user credentials, which introduces a single point of failure and significantly increases the risk of sensitive data exposure, even when cryptographic hashing techniques are employed. To overcome these limitations, this paper proposes a novel and privacy-preserving authentication framework that eliminates the need for direct password storage and transmission. The proposed approach leverages a hybrid integration of three advanced technologies: blockchain, zero-knowledge proofs (ZKP), and artificial intelligence (AI), to provide a secure and efficient authentication mechanism. Blockchain technology is utilized to establish a decentralized and tamper-resistant environment for identity verification. By removing dependence on centralized authorities, the system ensures transparency, data integrity, and enhanced security. Furthermore, zero-knowledge proofs enable users to authenticate themselves without revealing their actual credentials, thereby preserving privacy and minimizing the risk of credential leakage during the authentication process. In addition to this, an AI-based anomaly detection module is incorporated to continuously monitor user behavior patterns, such as login time, device information, and geographical location. This intelligent module enables the system to detect suspicious activities in real time and apply adaptive, risk-based authentication strategies to prevent unauthorized access attempts. The proposed hybrid framework significantly enhances system security by eliminating traditional attack vectors associated with password-based systems, while also improving user privacy and overall reliability. Due to its decentralized and intelligent nature, the system is highly suitable for next-generation applications, including Web3 platforms, digital identity management systems, and secure online services.},
        keywords = {Artificial Intelligence, Authentication, Blockchain, Cybersecurity, Privacy, Zero-Knowledge Proofs},
        month = {July},
        }