Zhan Qin

University at Buffalo, State University of New York

Papers

Total Citations

145

H-Index

About

Zhan Qin is a leading researcher in privacy-preserving cloud computing and secure multimedia data processing, with a particular focus on protecting sensitive image data during outsourced computation. His pioneering work on efficient privacy-preserving image feature extraction in cloud environments has garnered significant attention, with his seminal 2014 paper on the topic accumulating 119 citations. In this foundational work, Qin addressed the critical challenge of enabling Scalar Invariant Feature Transform (SIFT)—a widely used algorithm for object recognition and robotic mapping—to operate securely on encrypted image data in the cloud. He further advanced this field with his 2016 SecSIFT framework, which achieved 26 citations by providing a more robust and practical solution for secure feature detection. Qin’s contributions are particularly impactful for enterprises and individuals increasingly reliant on cloud computing for computationally intensive tasks, as his methods balance efficiency with strong privacy guarantees. His research has helped establish a foundation for secure multimedia outsourcing, influencing subsequent work in privacy-preserving machine learning and secure image retrieval.