Privacy preservation and security challenges: a new frontier multimodal machine learning research
Santosh Kumar, Mithilesh Kumar Chaube, Srinivas Naik Nenavath, Sachin Kumar Gupta, Sumit Kumar Tetarave
Abstract
Multimodal machine learning is a vibrant multi-disciplinary field and achieved much attention due to its wide range of applications. A research problem is multimodal, for the impact of privacy preservation. It shields sensitive data in the cloud by using a single modality-based privacy system. The user's biometric features are always stored in the database, primarily present in the cloud server to validate the user and his access. This facet provides a beneficial quality but at the same time has raised crucial affairs in security and privacy of biometric feature set. The main concern is to manage and stop the privacy breaches in clouds. The article discusses the detailed analysis of security schemes with a multimodal-based learning framework over sensitive data and systems at both ends. The article also accentuates frameworks and schemes that may apply in various applications to ensure privacy preservation of individuals and data security by multimodal algorithms.