Developing a multimodal AI-based framework for predicting occupant thermal comfort in air-conditioned buildings by integrating environmental sensor data, thermal imaging, and visual information. The research focuses on combining these diverse data sources to understand occupant comfort and develop accurate, adaptive, and energy-efficient thermal comfort prediction models.
Research focused on data security and privacy in cloud computing, with emphasis on secure data sharing, data integrity verification, privacy preservation, and remote auditing. The work explores cryptographic approaches including bilinear pairing, identity-based signatures, ring signatures, vector commitments, group key agreement, and blind signatures to protect sensitive information, verify remotely stored data, and enable secure auditing in shared and dynamic cloud environments.
Recent Publications
Cloud-based data sharing supports collaboration but raises concerns over sensitive information. This paper proposes a secure group-sharing scheme using block design-based key agreement to protect sensitive data and verify its integrity.
Cloud Storage as a Service enables remote data storage but raises privacy and integrity concerns. This paper proposes third-party auditing using bilinear pairing to verify remote data integrity, with aggregation to improve efficiency.
Cloud storage offers scalable data services but raises privacy and integrity concerns. This paper proposes a security approach that protects stored data from unauthorized access and modification, ensuring that only authorized users can access it.
Cloud storage enables data outsourcing but raises integrity and privacy concerns. This paper proposes secure remote auditing using vector commitments, identity-based ring signatures, and group key agreement to verify outsourced data efficiently.