Chang Wen Chen

University at Buffalo, State University of New York

Papers

Total Citations

153

H-Index

About

Chang Wen Chen is a leading researcher in multimedia security, cloud computing, and privacy-preserving image processing. His work addresses the critical challenge of securely outsourcing computationally intensive tasks—such as image feature extraction—to cloud platforms without compromising sensitive data. Chen’s most impactful contribution is his pioneering work on privacy-preserving SIFT (Scale-Invariant Feature Transform), notably through the "Towards Efficient Privacy-preserving Image Feature Extraction in Cloud Computing" paper, which has garnered 119 citations. This research enables secure, efficient object recognition and robotic mapping in cloud environments, balancing utility with confidentiality. He further advanced this domain with "SecSIFT" (26 citations), refining protocols for encrypted image feature detection. Chen also contributed to early mobile multimedia standards with his work on "Multimedia over Mobile IP" (8 citations), reflecting his foresight in wireless communication. His research has significant implications for cloud security, IoT, and mobile applications, making him a key figure in bridging multimedia processing with cryptographic privacy. With a career spanning foundational and applied contributions, Chen’s work continues to influence secure cloud-based multimedia systems.