A Platform for Anonymous Tip-Off and Evidence Validation

In this modern world, the crime rate is rising rapidly, however, people are reluctant to share tip-offs (Evidence) with the investigating authorities, this is mainly because their personal information and identity may get compromised, posing a threat to their own personal safety. The paper proposes to build a centralized web-based platform for anonymous tip-off reporting and validation that aims to address the challenges faced by authorities in handling such information and provide an effective solution using technologies like Web Development, Machine Learning, and Cryptography. Tip-offs can be provided in the format of images, videos, texts, and audio files. The submitted tip-offs will be validated using Machine learning algorithms like SVM (Support Vector Machine), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and Natural Language Processing methodologies for validation and relevance to the information given by the user. Further, the whole data pipeline will be secured by AES (Advanced Encryption Standard) cryptographic algorithm. If the information is deemed to be credible and actionable, it is forwarded to the relevant authorities which will further take action towards it. The suggested approach has shown significant improvement in performance compared to existing methods and makes it accessible to the user to submit tip-offs at any given point in time