AI-Powered Cyberbullying Detection and Prevention for Safer Digital Spaces
By Soumiya Marichelvan, Aitizaz Ali, Uzair Ali
PDFAbstract
This research aims to address the growing issue of cyberbullying, particularly among youth, by developing an AI-powered system for real-time detection and prevention on digital platforms. The research focuses on understanding the various forms of cyberbullying and their detrimental effects on mental health. Using the Agile system development methodology, the research will follow an iterative process involving design, development, and testing phases to create a reliable detection tool. The system is intended to identify harmful online behaviour and alert parents or guardians promptly. Preliminary results suggest that the tool will feature an intuitive user interface and effective algorithms capable of detecting abusive interactions. This research is significant, as it proposes a technological solution to a pressing social issue, aiming to enhance the safety and well-being of young people online. Additionally, the research aligns with SDG 16 Peace, Justice, and Strong Institutions by contributing to the protection of children from online harm and promoting safer, more just digital environments.
Secure Multi-Party Computation for Cross-Institutional Query Federation without Raw Data Exposure in Healthcare Databases
By Haider H. Razzaq
PDFAbstract
Cross-institutional healthcare research requires secure analysis of patient data distributed across hospitals while complying with the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). Existing federated query platforms restrict analyses to predefined aggregates and may expose institution-level statistics, whereas federated learning remains vulnerable to information leakage. This paper proposes a Secure Multi-Party Computation (SMPC) framework for privacy-preserving federated Structured Query Language (SQL) queries across multiple healthcare institutions without disclosing individual records or intermediate results. The framework introduces a federated query language and compiler that transforms queries into depth-bounded arithmetic circuits. It integrates verifiable secret sharing, Beaver-triple generation, and distributed key generation to support secure equality testing, comparison, record linkage, and aggregation. Protocol complexity and security guarantees are analyzed under semi-honest and malicious adversarial models, with malicious security approximately doubling communication requirements. An analytical cost model, calibrated using the SPDZ family of Multi-Party Computation (MPC) frameworks, evaluates scalability across 3–30 institutions and 1,000–500,000 patient records per institution. Results project that a representative cross-institutional linkage query requires less than one second and approximately 77 megabytes of communication for 15 institutions containing 10,000 records each. Four healthcare case studies demonstrate the framework’s potential applicability to rare-disease cohort discovery, drug-safety surveillance, and comparative-effectiveness research. The proposed framework provides a scalable approach to privacy-preserving healthcare analytics while supporting regulatory compliance and enabling secure collaborative research without centralizing sensitive patient information.