Abstract

Citation recommendation (CR) systems for manuscripts employ automated systems to identify and suggest relevant scientific publications for effectively citing specific text passages; however, they face challenges related to the promotion of unreliable sources and the privacy of sensitive data. To address these limitations, a novel Dual Cloud Sturnus Optimized Intent Learning BERT with Randomized-Adversarial Secure Anonymization (SIL-BERT) based Bibliographic Network Recommender (BNR) is proposed in this study to enable secure citation recommendation. Among these issues, shilling attacks are particularly important because malicious actors exploit the mathematical foundations of collaborative filtering and manipulate the algorithms. To address this issue, a novel Sturnus Optimized Self-Intent BERT (SS-IBERT) is employed and it effectively reduces artificial inflation caused by algorithmic rhetoric and thereby mitigates the suppression of emerging researchers. Moreover, domain-specific interactions combined with re-identification through linkable attributes generate a distinctive chronological activity fingerprint, which allows anonymized citation logs to be cross-referenced with public metadata. Therefore, to deal with this, a Randomized-Style Adversarial Anonymization (R-SAA) is used, and it effectively suppresses the Small-N Behavioral Vulnerability (S-NBV) that creates elevated re-identification risks. The experimental results demonstrate that the proposed Dual Cloud SIL-BERT achieves a citation-recommendation accuracy of approximately 99%.

Keywords

Citation Recommendation, Natural Language Processing, Bi-directional Encoder Representations from Transformers, Deep Learning and Cloud-based security,

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References

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