A Survey on Crime Detection and Prevention Techniques using Modern Computational Approaches

In the digital age, crime management systems have evolved significantly, integrating technologies such as cloud computing, secure databases, facial recognition, and real-time communication.This paper presents a comprehensive survey of research efforts focused on digital crime reporting, predictive analysis, and intelligent surveillance.Key contributions from recent literature include encrypted data storage for sensitive criminal records, use of machine learning for pattern detection, and secure mobile-based citizen reporting mechanisms.Techniques like facial detection using MTCNN, end-to-end encryption via Signal Protocol, and hybrid CNN-SVM models for incident classification have been explored.We also examine cloud-based storage architectures emphasizing data integrity and access control.The challenges of metadata leakage, privacy trade-offs, and model scalability are discussed.This survey aims to provide a foundational understanding of current methodologies and their effectiveness, while identifying potential areas for improvement in creating scalable, secure, and citizen-friendly crime management infrastructures.