Security issues and mechanisms in multimodal data

Abstract

There are several types of multimodal data such as text, image, audio, video, and sensor streams. It is evolving at a very high rate, which has resulted in not only complicated security issues but also enormous potential opportunities in advanced analytics and intelligent applications. The significant security issues in multimodal data management are analyzed in this chapter, such as data integrity, data confidentiality, authentication, access control, and privacy protection. Multimodal data is vulnerable to attacks such as adversarial attacks, data breaches, unauthorized access, and cross-modal inference, and due to its dispersed and heterogeneous nature, such attacks can compromise sensitive information. The chapter examines diverse security controls to address these challenges, such as safe multimodal fusion, blockchain architecture, cryptography, federated learning, differential privacy, and robust authentication processes. It is concentrated on scalable and real-time protection methods that are effective in edge-cloud designs and big data environments. This combination of approaches will enable businesses to ensure dependable multimodal data analytics that will safeguard user privacy and system reliability and allow making safe and efficient decisions in any field of application.